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Publisher: Elsevier   (Total: 3157 journals)

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Showing 1401 - 1600 of 3161 Journals sorted alphabetically
Intl. J. of Accounting     Hybrid Journal   (Followers: 1)
Intl. J. of Accounting Information Systems     Hybrid Journal   (Followers: 5, SJR: 0.399, CiteScore: 2)
Intl. J. of Adhesion and Adhesives     Hybrid Journal   (Followers: 19, SJR: 0.926, CiteScore: 2)
Intl. J. of Africa Nursing Sciences     Open Access   (SJR: 0.396, CiteScore: 1)
Intl. J. of Antimicrobial Agents     Hybrid Journal   (Followers: 10, SJR: 1.699, CiteScore: 4)
Intl. J. of Applied Earth Observation and Geoinformation     Hybrid Journal   (Followers: 35, SJR: 1.591, CiteScore: 4)
Intl. J. of Approximate Reasoning     Hybrid Journal   (Followers: 1, SJR: 0.866, CiteScore: 3)
Intl. J. of Biochemistry & Cell Biology     Hybrid Journal   (Followers: 6, SJR: 1.492, CiteScore: 3)
Intl. J. of Biological Macromolecules     Hybrid Journal   (Followers: 2, SJR: 0.917, CiteScore: 4)
Intl. J. of Cardiology     Hybrid Journal   (Followers: 16, SJR: 1.2, CiteScore: 2)
Intl. J. of Chemical and Analytical Science     Full-text available via subscription   (Followers: 4)
Intl. J. of Child-Computer Interaction     Hybrid Journal   (Followers: 2, SJR: 0.479, CiteScore: 3)
Intl. J. of Clinical and Health Psychology     Open Access   (Followers: 20, SJR: 1.345, CiteScore: 4)
Intl. J. of Coal Geology     Hybrid Journal   (Followers: 4, SJR: 2.186, CiteScore: 5)
Intl. J. of Critical Infrastructure Protection     Hybrid Journal   (Followers: 8, SJR: 0.648, CiteScore: 2)
Intl. J. of Dental Science and Research     Full-text available via subscription   (Followers: 1)
Intl. J. of Developmental Neuroscience     Hybrid Journal   (Followers: 8, SJR: 0.986, CiteScore: 2)
Intl. J. of Diabetes Mellitus     Open Access   (Followers: 9)
Intl. J. of Disaster Risk Reduction     Hybrid Journal   (Followers: 19, SJR: 0.769, CiteScore: 2)
Intl. J. of Drug Policy     Hybrid Journal   (Followers: 460, SJR: 1.441, CiteScore: 3)
Intl. J. of e-Navigation and Maritime Economy     Open Access   (Followers: 3)
Intl. J. of Educational Development     Hybrid Journal   (Followers: 14, SJR: 0.822, CiteScore: 1)
Intl. J. of Educational Research     Hybrid Journal   (Followers: 27, SJR: 0.617, CiteScore: 1)
Intl. J. of Electrical Power & Energy Systems     Open Access   (Followers: 25, SJR: 1.276, CiteScore: 5)
Intl. J. of Engineering Science     Hybrid Journal   (Followers: 5, SJR: 2.82, CiteScore: 6)
Intl. J. of Fatigue     Hybrid Journal   (Followers: 38, SJR: 1.402, CiteScore: 3)
Intl. J. of Food Microbiology     Hybrid Journal   (Followers: 15, SJR: 1.366, CiteScore: 4)
Intl. J. of Forecasting     Hybrid Journal   (Followers: 28, SJR: 1.879, CiteScore: 3)
Intl. J. of Gastronomy and Food Science     Open Access   (Followers: 4, SJR: 0.422, CiteScore: 1)
Intl. J. of Gerontology     Open Access   (Followers: 8, SJR: 0.215, CiteScore: 0)
Intl. J. of Greenhouse Gas Control     Partially Free   (Followers: 6, SJR: 1.458, CiteScore: 4)
Intl. J. of Heat and Fluid Flow     Hybrid Journal   (Followers: 36, SJR: 0.947, CiteScore: 3)
Intl. J. of Heat and Mass Transfer     Hybrid Journal   (Followers: 278, SJR: 1.498, CiteScore: 4)
Intl. J. of Hospitality Management     Hybrid Journal   (Followers: 20, SJR: 2.027, CiteScore: 4)
Intl. J. of Human-Computer Studies     Hybrid Journal   (Followers: 18, SJR: 0.605, CiteScore: 3)
Intl. J. of Hydrogen Energy     Partially Free   (Followers: 20, SJR: 1.116, CiteScore: 4)
Intl. J. of Hygiene and Environmental Health     Hybrid Journal   (Followers: 7, SJR: 1.334, CiteScore: 4)
Intl. J. of Impact Engineering     Hybrid Journal   (Followers: 9, SJR: 2.124, CiteScore: 4)
Intl. J. of Industrial Ergonomics     Hybrid Journal   (Followers: 15, SJR: 0.795, CiteScore: 2)
Intl. J. of Industrial Organization     Hybrid Journal   (Followers: 24, SJR: 0.873, CiteScore: 1)
Intl. J. of Infectious Diseases     Open Access   (Followers: 8, SJR: 1.514, CiteScore: 3)
Intl. J. of Information Management     Hybrid Journal   (Followers: 312, SJR: 1.373, CiteScore: 6)
Intl. J. of Intercultural Relations     Hybrid Journal   (Followers: 13, SJR: 0.732, CiteScore: 2)
Intl. J. of Law and Psychiatry     Hybrid Journal   (Followers: 9, SJR: 0.546, CiteScore: 1)
Intl. J. of Law, Crime and Justice     Hybrid Journal   (Followers: 58, SJR: 0.362, CiteScore: 1)
Intl. J. of Machine Tools and Manufacture     Hybrid Journal   (Followers: 7, SJR: 2.7, CiteScore: 6)
Intl. J. of Management Education     Hybrid Journal   (Followers: 8, SJR: 0.597, CiteScore: 2)
Intl. J. of Marine Energy     Full-text available via subscription   (Followers: 1, SJR: 0.92, CiteScore: 2)
Intl. J. of Mass Spectrometry     Hybrid Journal   (Followers: 17, SJR: 0.61, CiteScore: 2)
Intl. J. of Mechanical Sciences     Hybrid Journal   (Followers: 13, SJR: 1.595, CiteScore: 4)
Intl. J. of Medical Informatics     Hybrid Journal   (Followers: 9, SJR: 1.247, CiteScore: 4)
Intl. J. of Medical Microbiology     Hybrid Journal   (Followers: 8, SJR: 1.717, CiteScore: 4)
Intl. J. of Mineral Processing     Hybrid Journal   (Followers: 10, SJR: 0.782, CiteScore: 2)
Intl. J. of Mining Science and Technology     Open Access   (Followers: 3, SJR: 1.323, CiteScore: 2)
Intl. J. of Multiphase Flow     Hybrid Journal   (Followers: 9, SJR: 1.218, CiteScore: 3)
Intl. J. of Naval Architecture and Ocean Engineering     Open Access   (Followers: 3, SJR: 0.571, CiteScore: 1)
Intl. J. of Neuropharmacology     Full-text available via subscription   (Followers: 1)
Intl. J. of Non-Linear Mechanics     Hybrid Journal   (Followers: 8, SJR: 1.032, CiteScore: 2)
Intl. J. of Nursing Sciences     Open Access   (Followers: 2, SJR: 0.285, CiteScore: 1)
Intl. J. of Nursing Studies     Hybrid Journal   (Followers: 15, SJR: 1.646, CiteScore: 4)
Intl. J. of Obstetric Anesthesia     Full-text available via subscription   (Followers: 13, SJR: 0.717, CiteScore: 2)
Intl. J. of Oral and Maxillofacial Surgery     Hybrid Journal   (Followers: 8, SJR: 1.137, CiteScore: 2)
Intl. J. of Orthopaedic and Trauma Nursing     Hybrid Journal   (Followers: 11, SJR: 0.369, CiteScore: 1)
Intl. J. of Osteopathic Medicine     Hybrid Journal   (Followers: 2, SJR: 0.297, CiteScore: 1)
Intl. J. of Paleopathology     Partially Free   (Followers: 8, SJR: 0.618, CiteScore: 1)
Intl. J. of Pavement Research and Technology     Open Access   (Followers: 6, SJR: 0.311, CiteScore: 1)
Intl. J. of Pediatric Otorhinolaryngology     Full-text available via subscription   (Followers: 1, SJR: 0.783, CiteScore: 1)
Intl. J. of Pediatric Otorhinolaryngology Extra     Full-text available via subscription   (Followers: 1, SJR: 0.11, CiteScore: 0)
Intl. J. of Pediatrics and Adolescent Medicine     Open Access   (Followers: 1, SJR: 0.144, CiteScore: 1)
Intl. J. of Pharmaceutics     Hybrid Journal   (Followers: 36, SJR: 1.172, CiteScore: 4)
Intl. J. of Plasticity     Hybrid Journal   (Followers: 7, SJR: 3.395, CiteScore: 6)
Intl. J. of Pressure Vessels and Piping     Hybrid Journal   (Followers: 28, SJR: 0.981, CiteScore: 2)
Intl. J. of Production Economics     Hybrid Journal   (Followers: 15, SJR: 2.401, CiteScore: 5)
Intl. J. of Project Management     Hybrid Journal   (Followers: 49, SJR: 1.463, CiteScore: 5)
Intl. J. of Psychophysiology     Hybrid Journal   (Followers: 5, SJR: 1.157, CiteScore: 3)
Intl. J. of Radiation Oncology*Biology*Physics     Hybrid Journal   (Followers: 32, SJR: 2.485, CiteScore: 3)
Intl. J. of Refractory Metals and Hard Materials     Hybrid Journal   (Followers: 5)
Intl. J. of Refrigeration     Full-text available via subscription   (Followers: 5, SJR: 1.471, CiteScore: 3)
Intl. J. of Research in Marketing     Hybrid Journal   (Followers: 20, SJR: 2.528, CiteScore: 3)
Intl. J. of Rock Mechanics and Mining Sciences     Hybrid Journal   (Followers: 8, SJR: 2.259, CiteScore: 4)
Intl. J. of Sediment Research     Full-text available via subscription   (Followers: 3, SJR: 0.663, CiteScore: 2)
Intl. J. of Solids and Structures     Hybrid Journal   (Followers: 15, SJR: 1.295, CiteScore: 3)
Intl. J. of Spine Surgery     Hybrid Journal   (Followers: 3, SJR: 0.793, CiteScore: 2)
Intl. J. of Surgery     Hybrid Journal   (Followers: 8, SJR: 0.834, CiteScore: 3)
Intl. J. of Surgery Case Reports     Open Access   (Followers: 4, SJR: 0.26, CiteScore: 1)
Intl. J. of Surgery Open     Open Access   (SJR: 0.116, CiteScore: 0)
Intl. J. of Surgery Protocols     Open Access   (Followers: 1, SJR: 0.141, CiteScore: 1)
Intl. J. of Sustainable Built Environment     Open Access   (Followers: 5, SJR: 0.746, CiteScore: 3)
Intl. J. of the Sociology of Law     Hybrid Journal   (Followers: 18)
Intl. J. of Thermal Sciences     Hybrid Journal   (Followers: 18, SJR: 1.429, CiteScore: 4)
Intl. J. of Transportation Science and Technology     Open Access   (Followers: 10)
Intl. J. of Veterinary Science and Medicine     Open Access   (Followers: 4)
Intl. J. of Women's Dermatology     Open Access   (Followers: 1, SJR: 0.213, CiteScore: 0)
Intl. Medical Review on Down Syndrome     Full-text available via subscription  
Intl. Orthodontics     Full-text available via subscription   (Followers: 3, SJR: 0.239, CiteScore: 0)
Intl. Perspectives on Child and Adolescent Mental Health     Full-text available via subscription   (Followers: 5)
Intl. Review of Cell and Molecular Biology     Full-text available via subscription   (Followers: 6, SJR: 1.973, CiteScore: 4)
Intl. Review of Cytology     Full-text available via subscription  
Intl. Review of Economics & Finance     Hybrid Journal   (Followers: 26, SJR: 0.841, CiteScore: 2)
Intl. Review of Economics Education     Hybrid Journal   (Followers: 1, SJR: 0.632, CiteScore: 1)
Intl. Review of Financial Analysis     Hybrid Journal   (Followers: 7, SJR: 0.755, CiteScore: 2)
Intl. Review of Law and Economics     Hybrid Journal   (Followers: 22, SJR: 0.572, CiteScore: 1)
Intl. Review of Neurobiology     Full-text available via subscription   (Followers: 2, SJR: 1.497, CiteScore: 3)
Intl. Review of Research in Mental Retardation     Full-text available via subscription   (Followers: 7)
Intl. Soil and Water Conservation Research     Open Access   (SJR: 0.667, CiteScore: 2)
Intl. Strategic Management Review     Open Access   (Followers: 4)
Investigación en Educación Médica     Open Access  
Investigaciones de Historia Económica     Full-text available via subscription   (SJR: 0.264, CiteScore: 0)
Investigaciones Europeas de Dirección y Economía de la Empresa     Open Access  
IRBM     Full-text available via subscription   (SJR: 0.298, CiteScore: 1)
IRBM News     Full-text available via subscription   (SJR: 0.139, CiteScore: 0)
ISA Transactions     Full-text available via subscription   (Followers: 1, SJR: 1.115, CiteScore: 4)
iScience     Open Access  
ISPRS J. of Photogrammetry and Remote Sensing     Hybrid Journal   (Followers: 71, SJR: 3.169, CiteScore: 8)
Italian Oral Surgery     Full-text available via subscription   (Followers: 1)
ITBM-RBM     Full-text available via subscription   (Followers: 1)
ITBM-RBM News     Full-text available via subscription   (Followers: 1)
J. de Chirurgie Viscerale     Full-text available via subscription   (Followers: 1, SJR: 0.264, CiteScore: 0)
J. de Gynécologie Obstétrique et Biologie de la Reproduction     Full-text available via subscription  
J. de Mathématiques Pures et Appliquées     Full-text available via subscription   (Followers: 4, SJR: 3.571, CiteScore: 2)
J. de Mycologie Médicale / J. of Medical Mycology     Full-text available via subscription   (Followers: 2, SJR: 0.495, CiteScore: 2)
J. de Pédiatrie et de Puériculture     Full-text available via subscription   (SJR: 0.116, CiteScore: 0)
J. de Radiologie     Full-text available via subscription  
J. de Radiologie Diagnostique et Interventionnelle     Full-text available via subscription   (Followers: 2)
J. de Thérapie Comportementale et Cognitive     Full-text available via subscription   (SJR: 0.111, CiteScore: 0)
J. de Traumatologie du Sport     Full-text available via subscription   (Followers: 2, SJR: 0.152, CiteScore: 0)
J. des Anti-infectieux     Full-text available via subscription   (Followers: 2, SJR: 0.107, CiteScore: 0)
J. des Maladies Vasculaires     Full-text available via subscription  
J. Européen des Urgences     Full-text available via subscription   (Followers: 1)
J. Européen des Urgences et de Réanimation     Hybrid Journal   (SJR: 0.108, CiteScore: 0)
J. for Nature Conservation     Hybrid Journal   (Followers: 28, SJR: 0.894, CiteScore: 2)
J. for Nurse Practitioners     Hybrid Journal   (Followers: 12, SJR: 0.179, CiteScore: 0)
J. Français d'Ophtalmologie     Full-text available via subscription   (Followers: 3, SJR: 0.292, CiteScore: 0)
J. of Academic Librarianship     Hybrid Journal   (Followers: 1052, SJR: 1.224, CiteScore: 2)
J. of Accounting and Economics     Hybrid Journal   (Followers: 39, SJR: 6.875, CiteScore: 4)
J. of Accounting and Public Policy     Hybrid Journal   (Followers: 7, SJR: 0.91, CiteScore: 2)
J. of Accounting Education     Hybrid Journal   (Followers: 6, SJR: 0.882, CiteScore: 1)
J. of Accounting Literature     Hybrid Journal   (Followers: 7, SJR: 0.986, CiteScore: 3)
J. of Acupuncture and Meridian Studies     Open Access   (Followers: 1, SJR: 0.347, CiteScore: 1)
J. of Acute Medicine     Open Access   (SJR: 0.196, CiteScore: 1)
J. of Adolescence     Hybrid Journal   (Followers: 15, SJR: 1.01, CiteScore: 2)
J. of Adolescent Health     Hybrid Journal   (Followers: 24, SJR: 1.851, CiteScore: 4)
J. of Advanced Research     Open Access   (Followers: 2, SJR: 0.741, CiteScore: 4)
J. of Aerosol Science     Hybrid Journal   (Followers: 5, SJR: 0.828, CiteScore: 3)
J. of Affective Disorders     Hybrid Journal   (Followers: 18, SJR: 2.053, CiteScore: 4)
J. of African Earth Sciences     Hybrid Journal   (Followers: 11, SJR: 0.681, CiteScore: 2)
J. of African Trade     Open Access  
J. of Aging Studies     Hybrid Journal   (Followers: 11, SJR: 0.8, CiteScore: 2)
J. of Air Transport Management     Hybrid Journal   (Followers: 9, SJR: 0.981, CiteScore: 2)
J. of Algebra     Full-text available via subscription   (Followers: 5, SJR: 1.187, CiteScore: 1)
J. of Algorithms     Full-text available via subscription   (Followers: 4)
J. of Allergy and Clinical Immunology     Hybrid Journal   (Followers: 31, SJR: 5.049, CiteScore: 7)
J. of Allergy and Clinical Immunology : In Practice     Full-text available via subscription   (Followers: 13, SJR: 1.461, CiteScore: 3)
J. of Alloys and Compounds     Hybrid Journal   (Followers: 13, SJR: 1.02, CiteScore: 4)
J. of American Association for Pediatric Ophthalmology and Strabismus     Hybrid Journal   (Followers: 7, SJR: 0.752, CiteScore: 1)
J. of Analytical and Applied Pyrolysis     Hybrid Journal   (Followers: 3, SJR: 1.129, CiteScore: 4)
J. of Anesthesia History     Full-text available via subscription   (Followers: 1, SJR: 0.19, CiteScore: 0)
J. of Anthropological Archaeology     Hybrid Journal   (Followers: 79, SJR: 1.24, CiteScore: 2)
J. of Anxiety Disorders     Hybrid Journal   (Followers: 16, SJR: 2.043, CiteScore: 4)
J. of Applied Biomedicine     Open Access   (Followers: 2, SJR: 0.348, CiteScore: 2)
J. of Applied Developmental Psychology     Hybrid Journal   (Followers: 14, SJR: 1.339, CiteScore: 3)
J. of Applied Economics     Full-text available via subscription   (Followers: 8, SJR: 0.235, CiteScore: 1)
J. of Applied Geophysics     Hybrid Journal   (Followers: 15, SJR: 0.636, CiteScore: 2)
J. of Applied Logic     Full-text available via subscription   (SJR: 0.277, CiteScore: 1)
J. of Applied Mathematics and Mechanics     Full-text available via subscription   (Followers: 9, SJR: 0.321, CiteScore: 0)
J. of Applied Research and Technology     Open Access   (SJR: 0.255, CiteScore: 1)
J. of Applied Research in Memory and Cognition     Partially Free   (Followers: 12, SJR: 1.303, CiteScore: 2)
J. of Applied Research on Medicinal and Aromatic Plants     Hybrid Journal   (SJR: 0.355, CiteScore: 2)
J. of Approximation Theory     Hybrid Journal   (Followers: 1, SJR: 0.907, CiteScore: 1)
J. of Archaeological Science     Hybrid Journal   (Followers: 66, SJR: 1.885, CiteScore: 3)
J. of Archaeological Science : Reports     Hybrid Journal   (Followers: 17, SJR: 0.659, CiteScore: 1)
J. of Arid Environments     Hybrid Journal   (Followers: 14, SJR: 0.763, CiteScore: 2)
J. of Arrhythmia     Open Access   (SJR: 0.398, CiteScore: 1)
J. of Arthroplasty     Hybrid Journal   (Followers: 50, SJR: 2.373, CiteScore: 3)
J. of Arthroscopy and Joint Surgery     Full-text available via subscription   (Followers: 2, SJR: 0.103, CiteScore: 0)
J. of Asia-Pacific Biodiversity     Open Access   (SJR: 0.361, CiteScore: 1)
J. of Asia-Pacific Entomology     Full-text available via subscription   (Followers: 6, SJR: 0.373, CiteScore: 1)
J. of Asian Ceramic Societies     Open Access   (Followers: 2, SJR: 0.509, CiteScore: 2)
J. of Asian Earth Sciences     Hybrid Journal   (Followers: 13, SJR: 1.488, CiteScore: 3)
J. of Asian Economics     Hybrid Journal   (Followers: 1, SJR: 0.419, CiteScore: 1)
J. of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 155, SJR: 0.696, CiteScore: 2)
J. of Autoimmunity     Hybrid Journal   (Followers: 16, SJR: 2.046, CiteScore: 7)
J. of Ayurveda and Integrative Medicine     Open Access   (Followers: 3, SJR: 0.338, CiteScore: 1)
J. of Banking & Finance     Hybrid Journal   (Followers: 180)
J. of Basic & Applied Zoology : Physiology     Open Access   (Followers: 3)
J. of Behavior Therapy and Experimental Psychiatry     Hybrid Journal   (Followers: 4, SJR: 1.42, CiteScore: 3)
J. of Behavior, Health & Social Issues     Open Access   (Followers: 7)
J. of Behavioral and Experimental Economics     Full-text available via subscription   (Followers: 8, SJR: 0.593, CiteScore: 1)
J. of Behavioral and Experimental Finance     Full-text available via subscription   (Followers: 3, SJR: 0.475, CiteScore: 1)
J. of Biochemical and Biophysical Methods     Hybrid Journal   (Followers: 5)
J. of Biomechanics     Hybrid Journal   (Followers: 37, SJR: 1.147, CiteScore: 3)
J. of Biomedical Informatics     Partially Free   (Followers: 15, SJR: 1.028, CiteScore: 4)
J. of Biomedical Research     Full-text available via subscription   (Followers: 3, SJR: 0.712, CiteScore: 2)
J. of Bionic Engineering     Full-text available via subscription   (SJR: 0.584, CiteScore: 3)
J. of Bioscience and Bioengineering     Full-text available via subscription   (Followers: 31, SJR: 0.675, CiteScore: 2)
J. of Biotechnology     Hybrid Journal   (Followers: 62, SJR: 0.929, CiteScore: 3)
J. of Bodywork and Movement Therapies     Hybrid Journal   (Followers: 17, SJR: 0.522, CiteScore: 1)
J. of Bone Oncology     Open Access   (Followers: 1, SJR: 0.941, CiteScore: 3)
J. of Building Engineering     Hybrid Journal   (Followers: 2, SJR: 0.753, CiteScore: 3)
J. of Business Research     Hybrid Journal   (Followers: 22, SJR: 1.26, CiteScore: 3)

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Journal Cover
ISPRS Journal of Photogrammetry and Remote Sensing
Journal Prestige (SJR): 3.169
Citation Impact (citeScore): 8
Number of Followers: 71  
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0924-2716
Published by Elsevier Homepage  [3157 journals]
  • Improvement of photogrammetric accuracy by modeling and correcting the
           thermal effect on camera calibration
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): M. Daakir, Y. Zhou, M. Pierrot Deseilligny, C. Thom, O. Martin, E. Rupnik This paper presents a new method for improving the geometric accuracy of photogrammetric reconstruction by modeling and correcting the thermal effect on camera image sensor. The objective is to verify that when the temperature of image sensor varies during the acquisition, image deformation induced by the temperature change is quantifiable, modelisable and correctable. A temperature sensor integrated in the camera enables the measurement of image sensor temperature at exposure. It is therefore natural and appropriate to take this effect into account and to finally model and correct it after a calibration step. Nowadays, in cartography applications performed with UAV, the frame rate of acquisitions is continuously increasing. A high frame rate over a long acquisition time can result in an important temperature increase of the image sensor and thus introduces image deformations. The correction of the above-mentioned effect can improve the measurement accuracy. We present three methods to calibrate the thermal effect and experiments on two datasets are carried out to verify the improvement in terms of the photogrammetric accuracy.
  • Estimating canopy structure and biomass in bamboo forests using airborne
           LiDAR data
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Lin Cao, Nicholas C. Coops, Yuan Sun, Honghua Ruan, Guibin Wang, Jinsong Dai, Guanghui She The Bamboo species accounts for almost 1% of the Earth’s forested area with an exceptionally fast growth peaking up to 7.5–100 cm per day during the growing period, making it an unique species with respect to measuring and monitoring using conventional forest inventory tools. In addition their widespread coverage and quick growth make them a critical component of the terrestrial carbon cycle and for mitigating the impacts of climate change. In this study, the capability of using airborne Light Detection and Ranging (LiDAR) data for estimating canopy structure and biomass of Moso bamboo (Phyllostachys pubescens) was assessed, which is one of the most valuable and widely distributed bamboo species in the subtropical forests of south China. To do so, we first evaluated the accuracy of using LiDAR data to interpolate the underlying ground terrain under bamboo forests and developed uncertainty surfaces using both LiDAR-derived vegetation and topographic metrics and a Random Forest (RF) classifier. Second, we utilized Principal Component Analysis (PCA) to quantify the variation of the vertical distribution of LiDAR-derived effective Leaf Area Index (LAI) of bamboo stands, and fitted regression models between selected LiDAR metrics and the field-measured attributes such mean height, DBH and biomass components (i.e., culm, branch, foliage and aboveground biomass (AGB)) across a range of management strategies. Once models were developed, the results were spatially extrapolated and compared across the bamboo stands. Results indicated that the LiDAR interpolated DTMs were accurate even under the dense intensively managed bamboo stands (RMSE = 0.117–0.126 m) as well as under secondary stands (RMSE = 0.102 m) with rugged terrain and near-ground dense vegetation. The development of uncertainty maps of terrain was valuable when examining the magnitude and spatial distribution of potential errors in the DTMs. The middle height intervals (i.e., HI4 and HI5) within the bamboo cumulative effective LAI profiles explained more variances by PCA analysis in the bamboo stands. Moso bamboo AGB was well predicted by the LiDAR metrics (R2 = 0.59–0.87, rRMSE = 11.92–21.11%) with percentile heights (h25-h95) and the coefficient of variation of height (hcv) having the highest relative importances for estimating AGB and culm biomass. The hcv explained the most variance in branch and foliage biomass. According to the spatial extrapolation results, areas of relatively low biomass were found on secondary stands (AGB = 49.42 ± 14.16 Mg ha−1), whereas the intensively managed stands (AGB = 173.47 ± 34.16 Mg ha−1) have much higher AGB and biomass components, followed by the extensively managed bamboo stands (AGB = 67.61 ± 13.10 Mg ha−1). This study demonstrated the potential benefits of using airborne LiDAR to accurately derive high resolution DTMs, characterize vertical structure of canopy and estimate the magnitude and distribution of biomass within Moso bamboo forests, providing key data for regional ecological, environmental and global carbon cycle models.
  • Potential of nonlocally filtered pursuit monostatic TanDEM-X data for
           coastline detection
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Michael Schmitt, Gerald Baier, Xiao Xiang Zhu This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For this task, an unsupervised coastline detection procedure based on scale-space representations and K-medians clustering as well as morphological image post-processing is proposed. Since this procedure exploits a clear discriminability of “dark” and “bright” appearances of water and land surfaces, respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data acquired in pursuit monostatic mode is expected to provide a promising benefit. In addition, we investigate the benefit introduced by a utilization of a non-local InSAR filter for amplitude denoising and coherence estimation instead of a conventional box-car filter. Experiments carried out on real TanDEM-X pursuit monostatic data confirm our expectations and illustrate the advantage of the employed data configuration over conventional TanDEM-X products for automatic coastline detection.
  • Cloud removal in remote sensing images using nonnegative matrix
           factorization and error correction
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Xinghua Li, Liyuan Wang, Qing Cheng, Penghai Wu, Wenxia Gan, Lina Fang In the imaging process of optical remote sensing platforms, clouds are an inevitable barrier to the effective observation of sensors. To recover the original information covered by the clouds and the accompanying shadows, a nonnegative matrix factorization (NMF) and error correction method (S-NMF-EC) is proposed in this paper. Firstly, a cloud-free fused reference image is obtained by a reference image and two or more low-resolution images using the spatial and temporal nonlocal filter-based data fusion model (STNLFFM). Secondly, the cloud-free fused reference image is used to remove the cloud cover of the cloud-contaminated image based on NMF. Finally, the cloud removal result is further improved by error correction. It is worth noting that cloud detection is not required by S-NMF-EC, and the cloud-free information of the cloud-contaminated image is maximally retained. Both simulated and real-data experiments were conducted to validate the proposed S-NMF-EC method. Compared with other cloud removal methods, the results demonstrate that S-NMF-EC is visually and quantitatively effective (correlation coefficients ≥ 0.99) for the removal of thick clouds, thin clouds, and shadows.
  • How do people understand convenience-of-living in cities' A multiscale
           geographic investigation in Beijing
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Xiuyuan Zhang, Shihong Du, Jixian Zhang With the acceleration of global urbanization, especially for developing countries, more and more people live in cities, but their living environments are significantly different (e.g., slums and wealthy districts), resulting in massive social problems and attracting worldwide attention. This study aims at revealing the spatial heterogeneity of convenience-of-living (COL) in cities, and exploring how people consider COL from a geographic perspective. COL is defined based on the accessibility to diverse tangible amenities, e.g., parking lots, primary schools, and hospitals, and can be influenced by local built environments. To analyze COL, we first propose a segmentation method, i.e., fractal block net evolution, to spatially delineate analysis units for COL. This method can produce multiscale units adapting to different cognition scales of people. Then, we measure distances to diverse amenities as predictors and combine them with survey data by random forest regression, which can predict COL across Beijing city. As a result, a COL map is generated and clearly reports the spatial distribution of COL scores across this city which is highly heterogeneous but also has a large spatial autocorrelation. In addition, the experimental results indicate people that (1) mainly consider a neighboring scale while rating COL; (2) care more about bus/subway stations, restaurants, and shopping malls; and (3) follow some rules for scoring COL, which can be visualized by random forest. These findings contribute to explaining how people understand COL and assist infrastructure planning that can best satisfy the needs of local residents.
  • A random forest classifier based on pixel comparison features for urban
           LiDAR data
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Chisheng Wang, Qiqi Shu, Xinyu Wang, Bo Guo, Peng Liu, Qingquan Li The outstanding accuracy and spatial resolution of airborne light detection and ranging (LiDAR) systems allow for very detailed urban monitoring. Classification is a crucial step in LiDAR data processing, as many applications, e.g., 3D city modeling, building extraction, and digital elevation model (DEM) generation, rely on classified results. In this study, we present a novel LiDAR classification approach that uses simple pixel comparison features instead of the manually designed features used in many previous studies. The proposed features are generated by the computed height difference between two randomly selected neighboring pixels. In this way, the feature design does not require prior knowledge or human effort. More importantly, the features encode contextual information and are extremely quick to compute. We apply a random forest classifier to these features and a majority analysis postprocessing step to refine the classification results. The experiments undertaken in this study achieved an overall accuracy of 87.2%, which can be considered good given that only height information from the LiDAR data was used. The results were better than those obtained by replacing the proposed features with five widely accepted man-made features. We conducted algorithm parameter setting tests and an importance analysis to explore how the algorithm works. We found that the pixel pairs directing along the object structure and with a distance of the approximate object size can generate more discriminative pixel comparison features. Comparison with other benchmark results shows that this algorithm can approach the performance of state-of-the-art deep learning algorithms and exceed them in computational efficiency. We conclude that the proposed algorithm has high potential for urban LiDAR classification.
  • Retrieving leaf area index in discontinuous forest using ICESat/GLAS
           full-waveform data based on gap fraction model
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Xuebo Yang, Cheng Wang, Feifei Pan, Sheng Nie, Xiaohuan Xi, Shezhou Luo Leaf area index (LAI) is an important vegetation structure parameter in terrestrial ecosystem modeling. Although the spaceborne Geoscience Laser Altimeter System (GLAS) on board the Ice, Cloud and land Elevation Satellite (ICESat) has been proved to have potential for deriving forest LAI, previous methods were only applicable to estimate the effective LAI. In this study, a physical method based on the gap fraction model was proposed to retrieve the LAI correcting the between-crown clumping in discontinuous forest using GLAS full-waveform data. Landsat TM imagery was utilized as auxiliary data for providing crown cover information within the footprint. Using the gap probability from GLAS data and the crown coverage fraction from Landsat imagery, the method corrects the between-crown clumping, which has been proved to contribute most to the total clumping effect, and accurately estimates the LAI in discontinuous forest (R2 = 0.83, RMSE = 0.39, n = 47). Additionally, the forest LAI underestimation caused by between-crown clumping was analyzed in practice and theory. Results show that the between-crown clumping has a nonnegligible influence on forest LAI estimation in cases that the forest area within the footprint is close to the nonforest area, and the average LAI of individual tree is high. This study may shed some light on the development of clumping effect and quantitative LAI inversion models.
  • Seamline network generation based on foreground segmentation for
           orthoimage mosaicking
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Li Li, Jingmin Tu, Ye Gong, Jian Yao, Jie Li For multiple orthoimages mosaicking, the detection of an optimal seamline in an overlapped region and the generation of a seamline network are two key issues for creating a seamless and pleasant large-scale digital orthophoto map. In this paper, a novel system is proposed to generate the large-scale orthophoto by mosaicking multiple orthoimages via Graph cuts. The proposed system is comprised of two parts. In the first part, to ensure that the detected seamline avoids crossing the obvious objects, a novel foreground segmentation-based approach is proposed to detect the optimal seamline for two adjacent images. The foreground objects are segmented from the overlapped region at the superpixel level followed by the pixel-level seamline optimization. In the second part, we propose a novel seamline network generation approach to produce the large-scale orthophoto by mosaicking multiple orthoimages. The pairwise and junction regions extracted from the initial network are refined using two-label and multi-label Graph cuts, respectively. The key advantage of our proposed seamline network is that junction points can be automatically and optimally found using the multi-label Graph cuts. The experimental results on two groups of orthoimages show that our proposed system can generate high-quality seamline networks with less artifacts, and that it outperforms the state-of-the-art algorithm and the commercial software based on visual comparison and statistical evaluation.
  • A derivative-free optimization-based approach for detecting architectural
           symmetries from 3D point clouds
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Fan Xue, Weisheng Lu, Christopher J. Webster, Ke Chen Symmetry is ubiquitous in architecture, across both time and place. Automated architectural symmetry detection (ASD) from a data source is not only an intriguing inquiry in its own right, but also a step towards creation of semantically rich building and city information models with applications in architectural design, construction management, heritage conservation, and smart city development. While recent advances in sensing technologies provide inexpensive yet high-quality architectural 3D point clouds, existing methods of ASD from these data sources suffer several weaknesses including noise sensitivity, inaccuracy, and high computational loads. This paper aims to develop a novel derivative-free optimization (DFO)-based approach for effective ASD. It does so by firstly transforming ASD into a nonlinear optimization problem involving architectural regularity and topology. An in-house ODAS (Optimization-based Detection of Architectural Symmetries) approach is then developed to solve the formulated problem using a set of state-of-the-art DFO algorithms. Efficiency, accuracy, and robustness of ODAS are gauged from the experimental results on nine sets of real-life architectural 3D point clouds, with the computational time for ASD from 1.4 million points only 3.7 s and increasing in a sheer logarithmic order against the number of points. The contributions of this paper are threefold. Firstly, formulating ASD as a nonlinear optimization problem constitutes a methodological innovation. Secondly, the provision of up-to-date, open source DFO algorithms allows benchmarking in the future development of free, fast, accurate, and robust approaches for ASD. Thirdly, the ODAS approach can be directly used to develop building and city information models for various value-added applications.
  • Improving LiDAR classification accuracy by contextual label smoothing in
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Nan Li, Chun Liu, Norbert Pfeifer We propose a contextual label-smoothing method to improve the LiDAR classification accuracy in a post-processing step. Under the framework of global graph-structured regularization, we enhance the effectiveness of label smoothing from two aspects. First, each point can collect sufficient label-relevant neighborhood information to verify its label based on an optimal graph. Second, the input label probability set is improved by probabilistic label relaxation to be more consistent with the spatial context. With this optimal graph and reliable label probability set, the final labels are computed by graph-structured regularization. We demonstrate the contextual label-smoothing approach on two separate urban airborne LiDAR datasets with complex urban scenes. Significant improvements in the classification accuracies are achieved without losing small objects (such as façades and cars). The overall accuracy is increased by 7.01% on the Vienna dataset and 6.88% on the Vaihingen dataset. Moreover, most large, wrongly labeled regions are corrected by long-range interactions that are derived from the optimal graph, and misclassified regions that lack neighborhood communications in terms of correct labels are also corrected with the probabilistic label relaxation.
  • Analysis of urban surface morphologic effects on diurnal thermal
           directional anisotropy
    • Abstract: Publication date: February 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 148Author(s): Leiqiu Hu, Jochen Wendel Remote thermal radiative observations over metropolitan areas are subject to an angular-dependent variation, known as the directional thermal anisotropy. The 3D urban surface morphology is one key factor in determining the magnitude and temporal variation of thermal anisotropy. This study uses 3D building data and the Town Energy Balance model (TEB) to explore the impact of morphological variability on diurnal anisotropy patterns, and quantifies errors introduced by a simplification of urban morphology as an array of evenly distributed uniform cubes. Results from the comparison of two representative urban districts in Brooklyn and midtown Manhattan, New York City reveal distinct diurnal anisotropy patterns. Daytime anisotropy varies more time-sensitively over the compact high-rise district of Manhattan, although the maximum effective anisotropy (the maximum contrast of directional anisotropy) is smaller than Brooklyn, which is related to a reduced contrast among wall temperatures. A stronger angular effect at night is found as the aspect-ratio increases. The anisotropy is further simulated at the Moderate Resolution Imaging Spectroradiometer (MODIS) overpass time and sensor-surface relative geometry over these two morphologic samples. The sensitivity test unravels that the effective anisotropy monotonically increases with a greater aspect ratio for MODIS nighttime overpasses, while the daytime pattern is more complex with a single- or double-peak distribution depending on the solar angle (or time of day). Finally, the variation of building height and size is important in determining the anisotropy from comparing simulations of a realistic 3D building model and a simplified urban morphology as cube array. The morphological simplification can lead to a higher discrepancy in these cases with a high aspect ratio or small sky view factor for both daytime and nighttime. The proposed 3D-computer-graphics approach is computationally affordable for the seen surface estimation and can be applied to IFOVs across a relatively large urban area. Its flexibility in integrating various levels of 3D urban surface complexity makes it a promising tool for correcting the urban thermal anisotropy from satellite observations in the future.Graphical abstractGraphical abstract for this article
  • RETRACTED: A new On-orbit Geometric Self-calibration Approach for the
           High-resolution Multi-linear Array Optical Satellite Based on Stereoscopic
           Image Pairs
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Mi Wang, Yufeng Cheng, Luxiao He, Yuan Tian, Yanli Wang
  • Scale-variable region-merging for high resolution remote sensing image
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Tengfei Su In high resolution remote sensing imagery (HRI), the sizes of different geo-objects often vary greatly, posing serious difficulties to their successful segmentation. Although existent segmentation approaches have provided some solutions to this problem, the complexity of HRI may still lead to great challenges for previous methods. In order to further enhance the quality of HRI segmentation, this paper proposes a new segmentation algorithm based on scale-variable region merging. Scale-variable means that the scale parameters (SP) adopted for segmentation are adaptively estimated, so that geo-objects of various sizes can be better segmented out. To implement the proposed technique, 3 steps are designed. The first step produces a coarse-segmentation result with slight degree of under segmentation error. This is achieved by segmenting a half size image with the global optimal SP. Such a SP is determined by using the image of original size. In the second step, structural and spatial contextual information is extracted from the coarse-segmentation, enabling the estimation of variable SPs. In the last step, a region merging process is initiated, and the SPs used to terminate this process are estimated based on the information obtained in the second step. The proposed method was tested by using 3 scenes of HRI with different landscape patterns. Experimental results indicated that our approach produced good segmentation accuracy, outperforming some competitive methods in comparison.
  • Marker-free coregistration of UAV and backpack LiDAR point clouds in
           forested areas
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Przemyslaw Polewski, Wei Yao, Lin Cao, Sha Gao Unmanned aerial vehicle Laser Scanning (ULS) and Backpack Laser Scanning (BLS) are two emerging mobile mapping technologies applicable for monitoring forested environments in unprecedented detail from complementary perspectives. Although ground-based backpack techniques provide detailed information about the forest understory and terrain, the measured point clouds based on SLAM techniques are stitched together gradually and normally expressed in a less-accurate arbitrary coordinate system. Conversely, ULS point clouds are acquired from above and usually georeferenced, yet the point density and penetrability near the ground may still suffer from dense overstory despite the low attitude operation. Coregistering the ground and aerial point clouds in the ULS coordinate system therefore provides a method for fusing understory and overstory information at single tree level without the time consuming procedure of applying ground control points. Since the ULS and BLS acquisition viewpoints differ greatly, standard coregistration methods requiring 3D point-level correspondences are likely to fail. This paper presents an object-level coregistration approach which instead operates on two sets of tree positions, with the goal of finding the optimal 3D transformation (consisting of rotation, translation and scaling) between the respective coordinate systems. The entire task is decomposed into separate problems of computing the common Z axis, estimating the scale, and 2D coregistration. In contrast to existing methods, our approach does not require additional information such as tree diameters or heights. We evaluated our method on real test plots involving diverse stem densities and tree species situated in forest farm of the eastern coastal region of Jiangsu, China. The tree positions for ground and aerial data were obtained respectively by cylinder fitting and tree segmentation. On 3 coniferous (dawn redwood) plots, 46–81% trees were matched with a distance below 50 cm, and mean position deviation of 27–36 cm. For 4 broadleaf (poplar) plots, no more than 50% trees were matched below a 1 m threshold and mean error of 54–67 cm, which can be attributed to the broadleaf trees’ more irregular shape and lack of a well defined tree top. Moreover, we show that the introduction of scaling into the transform can increase the matched tree count by up to 20 percentage points and decrease the mean matched distance by up to 13% compared to a strictly rigid transform.
  • Evaluating the capability of the Sentinel 2 data for soil organic carbon
           prediction in croplands
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Fabio Castaldi, Andreas Hueni, Sabine Chabrillat, Kathrin Ward, Gabriele Buttafuoco, Bart Bomans, Kristin Vreys, Maximilian Brell, Bas van Wesemael The short revisit time of the Sentinel-2 (S2) constellation entails a large availability of remote sensing data, but S2 data have been rarely used to predict soil organic carbon (SOC) content. Thus, this study aims at comparing the capability of multispectral S2 and airborne hyperspectral remote sensing data for SOC prediction, and at the same time, we investigated the importance of spectral and spatial resolution through the signal-to-noise ratio (SNR), the variable importance in the prediction (VIP) models and the spatial variability of the SOC maps at field and regional scales. We tested the capability of the S2 data to predict SOC in croplands with quite different soil types and parent materials in Germany, Luxembourg and Belgium, using multivariate statistics and local ground calibration with soil samples. We split the calibration dataset into sub-regions according to soil maps and built a multivariate regression model within each sub-region. The prediction accuracy obtained by S2 data is generally slightly lower than that retrieved by airborne hyperspectral data. The ratio of performance to deviation (RPD) is higher than 2 in Luxembourg (2.6) and German (2.2) site, while it is 1.1 in the Belgian area. After the spectral resampling of the airborne data according to S2 band, the prediction accuracy did not change for four out of five of the sub-regions. The variable importance values obtained by S2 data showed the same trend as the airborne VIP values, while the importance of SWIR bands decreased using airborne data resampled according the S2 bands. These differences of VIP values can be explained by the loss of spectral resolution as compared to APEX data and the strong difference in terms of SNR between the SWIR region and other spectral regions. The investigation on the spatial variability of the SOC maps derived by S2 data has shown that the spatial resolution of S2 is adequate to describe SOC variability both within field and at regional scale.
  • Co-polarization channel imbalance phase estimation by
           corner-reflector-like targets
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Lei Shi, Pingxiang Li, Jie Yang, Liangpei Zhang, Xiaoli Ding, Lingli Zhao Polarimetric calibration is a critical step to suppress the potential system distortion before implementing any applications for polarimetric synthetic aperture radar (PolSAR). Among all the distortion elements, the crosstalk and cross-pol channel imbalance are generally estimated by the use of natural media, and the co-pol channel imbalance is traditionally solved by the use of corner reflectors (CRs). However, the deployment of ground CRs is costly and may even be impossible in some areas. Many bright point targets, such as poles, lamps, and corner points of structures, are commonly found in manmade regions. In particular, if the object orientation is parallel or perpendicular to the radar flight direction, some points will present similar polarimetric responses to trihedral or dihedral CRs. These points, which are referred to here as “CR-like targets”, can be treated as a supplement to approximately solve the system distortion elements when CRs are unavailable. In this paper, we propose a novel step-by-step algorithm to determine the CR-like targets and estimate the co-pol channel imbalance phase in uncalibrated PolSAR imagery. Chinese X-band airborne and C-band satellite PolSAR data were used to test the proposed method. Compared with the CR-derived co-pol channel imbalance phase, the solution errors of the CR-like targets were 1.305° and 0.03° for the X- and C-band experiments, respectively. The results of the experiments confirm that the solutions of the CR-like targets are very close to those of ground-deployed CRs, and the proposed method can be considered as an effective way to calibrate PolSAR images when sufficient CR-like point targets are detected in manmade regions.
  • Analogue-based colorization of remote sensing images using textural
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Mathieu Gravey, Luiz Gustavo Rasera, Gregoire Mariethoz Satellite images are richer than ever before. For example, new Landsat-8 images with their 11 bands carry much more information than older generations of satellites. These differences in spectral representation imply a major difficulty for assessing long-term land surface changes. The easiest solution is to reduce the information of the most recent product, for example by only keeping a subset of the Landsat-8 bands that matches old imagery. To avoid such loss of information, we propose a new method based on multiband spatial pattern matching. We are focusing on increasing the spectral resolution of archive satellite images to the same level of spectral resolution and coverage as modern imagery. Our method uses analogous scenes taken from modern satellites, which have conceptually the same role as the training images used in multiple-point geostatistics simulation. The spectral characteristics of the training image are then transferred to a target archive image, where new synthetic spectral bands are generated. A spatial pattern matching procedure is used to control this transfer, resulting in preservation of spatial and spectral coherence in the results. We illustrate the methodology on Landsat 8 and Corona imagery. The proposed method was benchmarked against other state-of-the-art colorization techniques, and it shows globally better results.
  • Correcting rural building annotations in OpenStreetMap using convolutional
           neural networks
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): John E. Vargas-Muñoz, Sylvain Lobry, Alexandre X. Falcão, Devis Tuia Rural building mapping is paramount to support demographic studies and plan actions in response to crisis that affect those areas. Rural building annotations exist in OpenStreetMap (OSM), but their quality and quantity are not sufficient for training models that can create accurate rural building maps. The problems with these annotations essentially fall into three categories: (i) most commonly, many annotations are geometrically misaligned with the updated imagery; (ii) some annotations do not correspond to buildings in the images (they are misannotations or the buildings have been destroyed); and (iii) some annotations are missing for buildings in the images (the buildings were never annotated or were built between subsequent image acquisitions). First, we propose a method based on Markov Random Field (MRF) to align the buildings with their annotations. The method maximizes the correlation between annotations and a building probability map while enforcing that nearby buildings have similar alignment vectors. Second, the annotations with no evidence in the building probability map are removed. Third, we present a method to detect non-annotated buildings with predefined shapes and add their annotation. The proposed methodology shows considerable improvement in accuracy of the OSM annotations for two regions of Tanzania and Zimbabwe, being more accurate than state-of-the-art baselines.
  • Measuring stem diameters with TLS in boreal forests by complementary
           fitting procedure
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Timo P. Pitkänen, Pasi Raumonen, Annika Kangas Point clouds generated by terrestrial laser scanners (TLS) have enabled new ways to measure stem diameters. A common method for diameter calculation is to fit cylindrical or circular shapes into the TLS point cloud, which can be based either on a single scan or a co-registered combination of several scans. However, as various defects in the point cloud may affect the final diameter results, we propose an automatized processing chain which takes advantage of complementing steps. Processing consists of two fitting phases and an additional taper curve calculation to define the final diameter measurements. First, stems are detected from co-registered data of several scans using surface normals and cylinder fitting. This provides a robust framework for localizing the stems and estimating diameters at various heights. Then, guided by the cylinders and their indicative diameters, another fitting round is performed by cutting the stems into thin horizontal slices and reassessing their diameters by circular shape. For each slice, the quality of the cylinder-modelled diameter is evaluated first with co-registered data and if it is found to be deficient, potentially due to modelling defects or co-registration errors, diameter is detected through single scans. Finally, slice diameters are applied to construct a spline-based taper curve model for each tree, which is used to calculate the final stem dimensions. This methodology was tested in southern Finland using a set of 505 trees. At the breast height level (1.3 m), the results indicate 5.2 mm mean difference (3.2%), −0.4 mm bias (-0.3%) and 7.3 mm root mean squared error (4.4%) to reference measurements, and at the height of 6.0 m, respective values are 6.5 mm (3.6%), +1.6 mm (0.9%) and 8.4 mm (4.8%). These values are smaller compared to most of the corresponding contemporary studies, and outperform the initial cylinder models. This indicates that the applied processing chain is capable of producing relatively accurate diameter measurements, which can, at the cost of computational heaviness, remove various defects and improve the modelling results.
  • Generating a hyperspectral digital surface model using a hyperspectral 2D
           frame camera
    • Abstract: Publication date: Available online 8 December 2018Source: ISPRS Journal of Photogrammetry and Remote SensingAuthor(s): Raquel A. Oliveira, Antonio M.G. Tommaselli, Eija Honkavaara Miniaturised 2D frame format hyperspectral camera technology that is suitable for small unmanned aerial vehicles (UAVs) has entered the market, making the generation of hyperspectral digital surface models (HDSMs) feasible. HDSMs offer a rigorous approach to capturing the target spectral and 3D geometric data. The main objective of this investigation was to study and develop techniques for the generation of HDSMs in forest areas using novel hyperspectral 2D frame camera technologies. An approach based on object-space image matching was developed, adapting the traditional vertical line locus (VLL) method for HDSM generation; this was then named the hyperspectral VLL (HVLL) approach. Additionally, image classification was introduced into the processing chain in order to adapt the matching parameters, based on different classes. We also proposed a method for extracting the spectral and viewing angle information of the points. An empirical study was carried out using UAV datasets from tropical and boreal forests using 2D format hyperspectral cameras, based on tuneable Fabry-Pérot interferometer (FPI) technology. Quality assessment was performed using DSMs based on state-of-the-art commercial software and airborne laser scanning (ALS). The results showed that the proposed technique generated a high-quality HDSM in both tested environments. The HDSM had higher deviations over the continuous canopy cover than the digital surface models (DSMs) generated using commercial software. The method using image classification information outperformed the commercial approach with respect to the ability to measure ground points in shadowed areas and in canopy gaps. The proposed method is of great interest in supporting automated interpretations of novel multi- and hyperspectral imaging technologies, especially when applied complex objects, such as forests.
  • Canopy penetration depth estimation with TanDEM-X and its compensation in
           temperate forests
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Michael Schlund, Daniel Baron, Paul Magdon, Stefan Erasmi The potential of X-band interferometric synthetic aperture radar (InSAR) heights (e.g. from the TanDEM-X mission) for vegetation canopy height estimation has long been recognized. However, the penetration of the X-band into the canopy results in a height bias and substantially affects this estimation. The aim of the study was to apply a physical model to compensate the penetration depth (i.e. height bias) in canopy height estimation and evaluate its performance. We applied a penetration depth model on different TanDEM-X data in three German forests. This model is based on the volume coherence and imaging geometry of the InSAR acquisitions. We extracted the volume coherence from the TanDEM-X data and retrieved the height bias based on the penetration depth compared to actual surface heights. The modeled height bias was used to compensate the height bias in InSAR heights. The corrected TanDEM-X heights were evaluated with LiDAR data. In general, the penetration depth compensation in the InSAR heights improved the performance compared to the original InSAR heights resulting in elevations with a lower root mean squared error and the mean error decreased to less than 1 m with the LiDAR heights used as reference. This suggested that the height bias was accurately modeled for temperate forests, which can be of high relevance when InSAR heights are used for canopy height estimation or used in multi-temporal analysis such as forest growth, degradation and deforestation monitoring.
  • Segmentation-aided classification of hyperspectral data using spatial
           dependency of spectral bands
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Annalisa Appice, Donato Malerba Classifying every pixel of a hyperspectral image with a certain land-cover type is the cornerstone of hyperspectral image analysis. In the present study a segmentation-aided methodology for the spectral-spatial classification of hyperspectral data is proposed. It considers the spatial dependence of the spectral bands, deals with the curse of dimensionality and handles the spectral variability. A local spatial regularization of spectral information is used, in order to derive an informative joint spectral-spatial representation of the data. A contiguity-based segmentation algorithm is formulated, in order to build the object-wise texture that can aid classifier learning. The hybrid use of the segmentation texture is evaluated in both pre-processing (i.e. selecting representative pixels to learn the classifier) and post-processing (i.e. refining predicted labels and removing possible outlier classifications). The experiments performed with the proposed methodology provide encouraging results, also compared to several recent state-of-the-art approaches.
  • Generating a series of land covers by assimilating the existing land cover
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Guang Xu, Baozhang Chen Land cover (LC), which describes the physical material of land surface, is an important parameter in earth system science. With more and more LC data available, this research aims to establish a data assimilation framework for integrating continuous LC time series based on the existing LC products. The framework is designed by borrowing the basic concept from data assimilation to find out the optimal LC time series based on existing observations. According to the observing system simulation experiments, the assimilation framework showed a good performance against noises and uncertainties. Moreover, after validation with the existing LC reference data, the averaged accuracy of the LC data produced using the assimilation framework was 73.7%, which is a great improvement compared to the existing LC maps considering the temporal continuity. This LC map assimilation framework would be useful for assessing the global LC changes and related researches.
  • Learnable manifold alignment (LeMA): A semi-supervised cross-modality
           learning framework for land cover and land use classification
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Danfeng Hong, Naoto Yokoya, Nan Ge, Jocelyn Chanussot, Xiao Xiang Zhu In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community—can a limited amount of highly-discriminative (e.g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discriminative (e.g., multispectral) data' Traditional semi-supervised manifold alignment methods do not perform sufficiently well for such problems, since the hyperspectral data is very expensive to be largely collected in a trade-off between time and efficiency, compared to the multispectral data. To this end, we propose a novel semi-supervised cross-modality learning framework, called learnable manifold alignment (LeMA). LeMA learns a joint graph structure directly from the data instead of using a given fixed graph defined by a Gaussian kernel function. With the learned graph, we can further capture the data distribution by graph-based label propagation, which enables finding a more accurate decision boundary. Additionally, an optimization strategy based on the alternating direction method of multipliers (ADMM) is designed to solve the proposed model. Extensive experiments on two hyperspectral-multispectral datasets demonstrate the superiority and effectiveness of the proposed method in comparison with several state-of-the-art methods.
  • A deep learning framework for road marking extraction, classification and
           completion from mobile laser scanning point clouds
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Chenglu Wen, Xiaotian Sun, Jonathan Li, Cheng Wang, Yan Guo, Ayman Habib Road markings play a critical role in road traffic safety and are one of the most important elements for guiding autonomous vehicles (AVs). High-Definition (HD) maps with accurate road marking information are very useful for many applications ranging from road maintenance, improving navigation, and prediction of upcoming road situations within AVs. This paper presents a deep learning-based framework for road marking extraction, classification and completion from three-dimensional (3D) mobile laser scanning (MLS) point clouds. Compared with existing road marking extraction methods, which are mostly based on intensity thresholds, our method is less sensitive to data quality. We added the step of road marking completion to further optimize the results. At the extraction stage, a modified U-net model was used to segment road marking pixels to overcome the intensity variation, low contrast and other issues. At the classification stage, a hierarchical classification method by integrating multi-scale clustering with Convolutional Neural Networks (CNN) was developed to classify different types of road markings with considerable differences. At the completion stage, a method based on a Generative Adversarial Network (GAN) was developed to complete small-size road markings first, then followed by completing broken lane lines and adding missing markings using a context-based method. In addition, we built a point cloud road marking dataset to train the deep network model and evaluate our method. The dataset contains urban road and highway MLS data and underground parking lot data acquired by our own assembled backpacked laser scanning system. Our experimental results obtained using the point clouds of different scenes demonstrated that our method is very promising for road marking extraction, classification and completion.
  • Multispectral change detection using multivariate Kullback-Leibler
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Shabnam Jabari, Mohammad Rezaee, Fatemeh Fathollahi, Yun Zhang Change detection is one of the most critical applications in remote sensing. However, distinguishing between changes and non-changes in images collected at different dates and different imaging platforms is challenging. This is because the image dissimilarities caused by the difference in imaging conditions can mislead the change detection algorithms and result in false alarms. This problem is even more severe in urban areas due to a wide range of urban objects that have different materials and spectral signatures. To overcome this problem, the majority of studies in the recent literature use information-based methods for change detection. However, these methods are limited to using only a single band for change detection, without utilizing the multispectral properties of optical remote sensing images. In this paper, we propose a change criterion that uses the multivariate expansion of the Kullback-Leibler divergence to overcome the non-linear imaging condition differences and to utilize the multispectral properties for optical change detection. The proposed change criterion measures the similarity between the multivariate probability density functions of the corresponding objects in two images. For probability density functions, a Gaussian distribution is used whose parameters are approximated by a maximum-likelihood estimation. The degree of similarity between the two probability density functions is given by the MultiVariate Kullback-Leibler distance. The higher the similarity, the lower the probability of change. We tested the proposed change criterion on four real and one simulated urban datasets. The results demonstrate that the proposed method is robust against excessive imaging condition differences and can significantly improve the change detection results.
  • Saliency detection of targets in polarimetric SAR images based on globally
           weighted perturbation filters
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Haiyi Yang, Zongjie Cao, Zongyong Cui, Yiming Pi In this paper, a saliency detection for Polarimetric Synthetic Aperture Radar (PolSAR) images is proposed based on weighted perturbation filters. Auxiliary data is demanded to identify polarimetric vector of targets, for a canonical perturbation filter. Only if the target signature was available and accurate, it would be satisfiable to apply the filter in practice. Besides, not every target can usually be detected by an individual filter, because of variant polarimetric characteristics of targets with respect to different aspects or shapes. To overcome these drawbacks, several perturbation filters are combined in the proposed method. By initializing with different parameters, these filters decompose PolSAR data into their index maps. Then, aiming to find out filters of interest, i.e., ones related to target pixels, we assume that targets to detect are sparse in PolSAR image. Thus, saliency weights are assigned to the filters, based on Jaccard distances of their index maps. Therein, the spatial sparseness between objects and their surrounding derives high weights for corresponding filters. And then, after globally fusion of refined filtering responses with the weights, saliency map is generated for every local pattern in PolSAR image. Finally, the target regions are extracted from this map, by thresholding and morphological operation. Experiments performed on real and simulated PolSAR data verify the performance of this method, in comparison with several common PolSAR detectors. Also, the proposed method finds out most targets in ground truth, without auxiliary polarimetric information provided.
  • Simplification of geometric objects in an indoor space
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Joon-Seok Kim, Ki-Joune Li The interior of a building may be more complicated than its exterior because such an indoor space comprises a number of three-dimensional (3D) non-overlapping regions called cells (e.g. rooms). Owing to the complexity of 3D geometry, applications with 3D data (e.g. indoor navigation) require an adequate level of detail (LoD) to achieve their purpose. To supply the 3D data demanded by clients, a customised simplification that considers LoDs in an indoor space is required. Most research studies on the simplification of 3D objects, however, have focused on general 3D objects or the exterior of buildings. Applying such approaches to indoor space objects is inefficient and may cause loss of important information because cells in an indoor space have distinctive characteristics compared to general 3D objects. For instance, a conventional room is surrounded by vertically aligned walls and a horizontally aligned ceiling and floor. For this reason, we propose a dedicated simplification method of 3D geometric objects in an indoor space. Our method takes full advantage of the prism model, which is an alternative 3D geometric model that supports prismatic shapes motivated by traits of indoor spaces. Additionally, an approach for dealing with potential topological inconsistencies during simplification is presented in this paper. An empirical analysis of the efficiency of the proposed simplification is conducted to validate our work.
  • Modeling alpine grassland forage phosphorus based on hyperspectral remote
           sensing and a multi-factor machine learning algorithm in the east of
           Tibetan Plateau, China
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Jinlong Gao, Baoping Meng, Tiangang Liang, Qisheng Feng, Jing Ge, Jianpeng Yin, Caixia Wu, Xia Cui, Mengjing Hou, Jie Liu, Hongjie Xie The accurate and effective retrieval of forage phosphorus (P) content can provide significant information for the management of pastoral agriculture and grazing livestock. In this study, we constructed 39 models to estimate the forage P of alpine grassland in the east of Tibetan Plateau based on hyperspectral remote sensing and multiple factors (topography, soil, vegetation and meteorology) using a machine learning algorithm. The results show that (1) first derivative (FD) and continuum removal (CR) spectra can retrieve more feature bands that are mainly located in the near infrared (NIR) and shortwave infrared (SWIR) regions than log transformed (Log (1/R)) and original (OR) spectra for the forage P estimation; (2) in terms of the model precision, the combination of important bands (IBs) and important factors (longitude and monthly mean temperature) increase the accuracy of forage P estimation as compared with the models that used IBs alone; and (3) considering the precision, stability and simplicity of the model comprehensively, the FD-IBs + support vector machine (SVM) model is the optimum forage P inversion model, which presents coefficient of determination (R2) and root mean squared error (RMSE) values of 0.67 and 0.0472%, respectively, and standard deviations (SDs) of 0.2386 and 0.0050%, respectively. This model can account for 88% of the variation of forage P in alpine grassland. This study demonstrates the importance of using a multi-factor modeling approach and spectral transformation techniques for estimating the forage P of grasslands and provides a scientific basis for the reasonable use and management of alpine grassland resources.
  • Ailanthus altissima mapping from multi-temporal very high
           resolution satellite images
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Cristina Tarantino, Francesca Casella, Maria Adamo, Richard Lucas, Carl Beierkuhnlein, Palma Blonda This study presents the results of multi-seasonal WorldView-2 (WV-2) satellite images classification for the mapping of Ailanthus altissima (A. altissima), an invasive plant species thriving in a protected grassland area of Southern Italy. The technique used relied on a two-stage hybrid classification process: the first stage applied a knowledge-driven learning scheme to provide a land cover map (LC), including deciduous vegetation and other classes, without the need of reference training data; the second stage exploited a data-driven classification to: (i) discriminate pixels of the invasive species found within the deciduous vegetation layer of the LC map; (ii) determine the most favourable seasons for such recognition. In the second stage, when a traditional Maximum Likelihood classifier was used, the results obtained with multi-temporal July and October WV-2 images, showed an output Overall Accuracy (OA) value of ≈91%. To increase such a value, first a low-pass median filtering was used with a resulting OA of 99.2%, then, a Support Vector Machine classifier was applied obtaining the best A. altissima User’s Accuracy (UA) and OA values of 82.47% and 97.96%, respectively, without any filtering. When instead of the full multi-spectral bands set some spectral vegetation indices computed from the same months were used the UA and OA values decreased. The findings reported suggest that multi-temporal, very high resolution satellite imagery can be effective for A. altissima mapping, especially when airborne hyperspectral data are unavailable. Since training data are required only in the second stage to discriminate A. altissima from other deciduous plants, the use of the first stage LC mapping as pre-filter can render the hybrid technique proposed cost and time effective. Multi-temporal VHR data and the hybrid system suggested may offer new opportunities for invasive plant monitoring and follow up of management decision.
  • A multi-faceted CNN architecture for automatic classification of mobile
           LiDAR data and an algorithm to reproduce point cloud samples for enhanced
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Bhavesh Kumar, Gaurav Pandey, Bharat Lohani, Subhas C. Misra Mobile Laser Scanning (MLS) data of outdoor environment are typically characterised by occlusion, noise, clutter, large data size and high quantum of information which makes their classification a challenging problem. This paper presents three deep Convolutional Neural Network (CNN) architectures in three dimension (3D), namely single CNN (SCN), multi-faceted CNN (MFC) and MFC with reproduction (MFCR) for automatic classification of MLS data. The MFC uses multiple facets of an MLS sample as inputs to different SCNs, thus providing additional information during classification. The MFC, once trained, is used to reproduce additional samples with the help of existing samples. The reproduced samples are employed to further refine the MFC training parameters, thus giving a new method called MFCR. The three architectures are evaluated on an ensemble of 3D outdoor MLS data consisting of four classes, i.e. tree, pole, house and ground covered with low vegetation along with car samples from KITTI dataset. The total accuracy and kappa values of classifications reached up to (i) 86.0% and 81.3% for the SCN (ii) 94.3% and 92.4% for the MFC and (iii) 96.0% and 94.6% for the MFCR, respectively. The paper has demonstrated the use of multiple facets to significantly improve classification accuracy over the SCN. Finally, a unique approach has been developed for reproduction of samples which has shown potential to improve the accuracy of classification. Unlike previous works on the use of CNN for structured point cloud of indoor objects, this work shows the utility of different proposed CNN architectures for classification of varieties of outdoor objects, viz., tree, pole, house and ground which are captured as unstructured point cloud by MLS.
  • Practical optimal registration of terrestrial LiDAR scan pairs
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Zhipeng Cai, Tat-Jun Chin, Alvaro Parra Bustos, Konrad Schindler Point cloud registration is a fundamental problem in 3D scanning. In this paper, we address the frequent special case of registering terrestrial LiDAR scans (or, more generally, levelled point clouds). Many current solutions still rely on the Iterative Closest Point (ICP) method or other heuristic procedures, which require good initializations to succeed and/or provide no guarantees of success. On the other hand, exact or optimal registration algorithms can compute the best possible solution without requiring initializations; however, they are currently too slow to be practical in realistic applications.Existing optimal approaches ignore the fact that in routine use the relative rotations between scans are constrained to the azimuth, via the built-in level compensation in LiDAR scanners. We propose a novel, optimal and computationally efficient registration method for this 4DOF scenario. Our approach operates on candidate 3D keypoint correspondences, and contains two main steps: (1) a deterministic selection scheme that significantly reduces the candidate correspondence set in a way that is guaranteed to preserve the optimal solution; and (2) a fast branch-and-bound (BnB) algorithm with a novel polynomial-time subroutine for 1D rotation search, that quickly finds the optimal alignment for the reduced set. We demonstrate the practicality of our method on realistic point clouds from multiple LiDAR surveys.
  • Is field-measured tree height as reliable as believed – A comparison
           study of tree height estimates from field measurement, airborne laser
           scanning and terrestrial laser scanning in a boreal forest
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Yunsheng Wang, Matti Lehtomäki, Xinlian Liang, Jiri Pyörälä, Antero Kukko, Anttoni Jaakkola, Jingbin Liu, Ziyi Feng, Ruizhi Chen, Juha Hyyppä Quantitative comparisons of tree height observations from different sources are scarce due to the difficulties in effective sampling. In this study, the reliability and robustness of tree height observations obtained via a conventional field inventory, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) were investigated. A carefully designed non-destructive experiment was conducted that included 1174 individual trees in 18 sample plots (32 m × 32 m) in a Scandinavian boreal forest. The point density of the ALS data was approximately 450 points/m2. The TLS data were acquired with multi-scans from the center and the four quadrant directions of the sample plots. Both the ALS and TLS data represented the cutting edge point cloud products. Tree heights were manually measured from the ALS and TLS point clouds with the aid of existing tree maps. Therefore, the evaluation results revealed the capacities of the applied laser scanning (LS) data while excluding the influence of data processing approach such as the individual tree detection. The reliability and robustness of different tree height sources were evaluated through a cross-comparison of the ALS-, TLS-, and field- based tree heights. Compared to ALS and TLS, field measurements were more sensitive to stand complexity, crown classes, and species. Overall, field measurements tend to overestimate height of tall trees, especially tall trees in codominant crown class. In dense stands, high uncertainties also exist in the field measured heights for small trees in intermediate and suppressed crown class. The ALS-based tree height estimates were robust across all stand conditions. The taller the tree, the more reliable was the ALS-based tree height. The highest uncertainty in ALS-based tree heights came from trees in intermediate crown class, due to the difficulty of identifying treetops. When using TLS, reliable tree heights can be expected for trees lower than 15–20 m in height, depending on the complexity of forest stands. The advantage of LS systems was the robustness of the geometric accuracy of the data. The greatest challenges of the LS techniques in measuring individual tree heights lie in the occlusion effects, which lead to omissions of trees in intermediate and suppressed crown classes in ALS data and incomplete crowns of tall trees in TLS data.
  • Spectral-consistent relative radiometric normalization for multitemporal
           Landsat 8 imagery
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Muhammad Aldila Syariz, Bo-Yi Lin, Lino Garda Denaro, Lalu Muhamad Jaelani, Manh Van Nguyen, Chao-Hung Lin Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using multitemporal satellite images due to uncertainties of at-sensor radiances caused by different sun angles and atmospheric conditions. In case the atmospheric model and ground measurements are unavailable during data acquisitions, relative normalization is an alternative method which minimizes the radiometric differences among images without the requirement of additional information. The keys to a successful relative normalization are the selection of pseudo invariant features (PIFs) from bitemporal images and the regression of selected PIFs for transformation coefficient determination. Previous studies on transformation coefficient determination adopted band-by-band regression. These studies have obtained satisfactory normalization results; however, they have not fully considered the spectral inconsistency problem caused by individual band regression. To alleviate this problem, this study proposed a constrained orthogonal regression, which enforces pixel spectral signatures to be as consistent as possible during radiometric normalization while band regression quality is preserved. In addition, instead of selecting one of the input images as reference for radiometric transformation, a common radiometric level located between bitemporal images is selected as the reference to further reduce possible spectral inconsistency. Qualitative and quantitative analyses of several bitemporal images acquired by the Landsat 8 sensor were conducted to evaluate the proposed method with the measurements of spectral distance and similarity. The experimental results demonstrate the superiority of the proposed method to related regression and radiometric normalization methods, in terms of spectral signature consistency.
  • Structure from motion for ordered and unordered image sets based on random
           k-d forests and global pose estimation
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Xin Wang, Franz Rottensteiner, Christian Heipke In this paper, we present a new fast and robust method for structure from motion (SfM) for data sets potentially comprising thousands of ordered or unordered images. Our work focuses on the two most time-consuming procedures: (a) image matching and (b) pose estimation. For image matching, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely to overlap. For pose estimation, we use a two-stage global approach, separating the determination of rotation matrices and translation parameters; the latter are computed simultaneously using a new method. In order to cope with outliers in the relative orientations, which global approaches are particularly sensitive to, we present a new constraint based on triplet loop closure errors of rotation and translation. Finally, a robust bundle adjustment is carried out to refine the image orientation parameters.We demonstrate the potential and limitations of our pipeline using various real-world datasets including ordered image data acquired from UAV (unmanned aerial vehicle) and other platforms as well as unordered data from the internet. The experiments show that our work performs better than comparable state-of-the-art SfM systems in terms of run time, while we achieve a similar accuracy and robustness.
  • Efficient and robust lane marking extraction from mobile lidar point
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Jaehoon Jung, Erzhuo Che, Michael J. Olsen, Christopher Parrish Surveys of roadways with Mobile Laser Scanning (MLS) are now being conducted on a regular basis by many transportation agencies to provide detailed geometric information to support a wide range of applications, including asset management. Most MLS systems provide intensity (return signal strength) data as a point attribute in georeferenced point clouds, which may be used to estimate retro-reflectivity of pavement markings for effective maintenance. Nevertheless, the extraction of pavement markings from mobile lidar data remains an open challenge, due to variable noise, degree of wear on the markings, and road conditions. This paper addresses these challenges, presenting a novel approach for efficient, reliable extraction of lane markings, including those that have been significantly worn. First, using the MLS trajectory information, the lidar data is discretized into smaller sections, and then transformed to the local coordinate system, such that the road surface is near-horizontal for reliable extraction on roads with significant grade. Subsequently, the road surface is extracted using the constrained Random Sampling and Consensus (RANSAC) algorithm and then rasterized into a 2D intensity image to apply image processing techniques, namely: image segmentation to separate the lane markings from the road pavement, and a morphological opening operation to remove small objects. However, the extracted lane markings are prone to over-segmentation, due to occlusions or worn portions caused by moving vehicles. To rectify this, topologically-similar lane markings are associated with each other by computing line parameters (i.e., orientation and distance from the origin), which enables the gaps to be filled among the associated lanes. Finally, the remaining incorrect lane markings are detected and removed through a noise filtering phase using Dip test statistics. Examples of the effectiveness and application of the methodology are shown for a variety of sites with stripes of variable condition to highlight the robustness of the approach. Using optimized parameter values, the algorithm achieved F1 scores of 89–97% when tested on a variety of datasets encompassing a wide range of road scene types.
  • Aerial imagery for roof segmentation: A large-scale dataset towards
           automatic mapping of buildings
    • Abstract: Publication date: January 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 147Author(s): Qi Chen, Lei Wang, Yifan Wu, Guangming Wu, Zhiling Guo, Steven L. Waslander As an important branch of deep learning, convolutional neural network has largely improved the performance of building detection. For further accelerating the development of building detection toward automatic mapping, a benchmark dataset bears significance in fair comparisons. However, several problems still remain in the current public datasets that address this task. First, although building detection is generally considered equivalent to extracting roof outlines, most datasets directly provide building footprints as ground truths for testing and evaluation; the challenges of these benchmarks are more complicated than roof segmentation, as relief displacement leads to varying degrees of misalignment between roof outlines and footprints. On the other hand, an image dataset should feature a large quantity and high spatial resolution to effectively train a high-performance deep learning model for accurate mapping of buildings. Unfortunately, the remote sensing community still lacks proper benchmark datasets that can simultaneously satisfy these requirements. In this paper, we present a new large-scale benchmark dataset termed Aerial Imagery for Roof Segmentation (AIRS). This dataset provides a wide coverage of aerial imagery with 7.5 cm resolution and contains over 220,000 buildings. The task posed for AIRS is defined as roof segmentation. We implement several state-of-the-art deep learning methods of semantic segmentation for performance evaluation and analysis of the proposed dataset. The results can serve as the baseline for future work.
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