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Publisher: Elsevier   (Total: 3158 journals)

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Showing 1401 - 1600 of 3158 Journals sorted alphabetically
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: 36, 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: 7, 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: 17, 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: 20, SJR: 0.769, CiteScore: 2)
Intl. J. of Drug Policy     Hybrid Journal   (Followers: 467, 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: 28, SJR: 0.617, CiteScore: 1)
Intl. J. of Electrical Power & Energy Systems     Open Access   (Followers: 26, 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: 16, SJR: 1.366, CiteScore: 4)
Intl. J. of Forecasting     Hybrid Journal   (Followers: 29, 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: 300, 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: 21, 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: 25, 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: 329, SJR: 1.373, CiteScore: 6)
Intl. J. of Intercultural Relations     Hybrid Journal   (Followers: 14, 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: 57, 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: 10, SJR: 1.247, CiteScore: 4)
Intl. J. of Medical Microbiology     Hybrid Journal   (Followers: 9, 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: 16, 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: 2, SJR: 0.144, CiteScore: 1)
Intl. J. of Pharmaceutics     Hybrid Journal   (Followers: 37, 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: 18, SJR: 2.401, CiteScore: 5)
Intl. J. of Project Management     Hybrid Journal   (Followers: 50, SJR: 1.463, CiteScore: 5)
Intl. J. of Psychophysiology     Hybrid Journal   (Followers: 6, 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: 10, 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: 11)
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: 7)
Intl. Review of Cell and Molecular Biology     Full-text available via subscription   (Followers: 7, SJR: 1.973, CiteScore: 4)
Intl. Review of Cytology     Full-text available via subscription   (Followers: 1)
Intl. Review of Economics & Finance     Hybrid Journal   (Followers: 28, 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: 5)
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: 73, 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: 1065, 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: 17, 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: 12, 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: 10, 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: 78, 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 Geophysics     Hybrid Journal   (Followers: 17, 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: 14, SJR: 1.488, CiteScore: 3)
J. of Asian Economics     Hybrid Journal   (Followers: 3, SJR: 0.419, CiteScore: 1)
J. of Atmospheric and Solar-Terrestrial Physics     Hybrid Journal   (Followers: 165, 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: 186)
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: 4, 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: 16, 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: 3, SJR: 0.753, CiteScore: 3)
J. of Business Research     Hybrid Journal   (Followers: 22, SJR: 1.26, CiteScore: 3)
J. of Business Venturing     Hybrid Journal   (Followers: 26, SJR: 5.212, CiteScore: 9)
J. of Business Venturing Insights     Hybrid Journal   (Followers: 1, SJR: 1.162, CiteScore: 2)
J. of Cancer Policy     Open Access   (Followers: 2, SJR: 0.459, CiteScore: 1)
J. of Cancer Research and Practice     Open Access  

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Similar Journals
Journal Cover
ISPRS Journal of Photogrammetry and Remote Sensing
Journal Prestige (SJR): 3.169
Citation Impact (citeScore): 8
Number of Followers: 73  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0924-2716
Published by Elsevier Homepage  [3158 journals]
  • The U. V. Helava Award – Best Paper Volumes 135-146 (2018)
    • Abstract: Publication date: June 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 152Author(s):
       
  • A novel framework to detect conventional tillage and no-tillage cropping
           system effect on cotton growth and development using multi-temporal UAS
           data
    • Abstract: Publication date: June 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 152Author(s): Akash Ashapure, Jinha Jung, Junho Yeom, Anjin Chang, Murilo Maeda, Andrea Maeda, Juan Landivar Recent years have witnessed enormous interest in the application of Unmanned Aerial Systems (UAS) for precision agriculture. This study presents a novel approach to use multi-temporal UAS data for comparison of two management practices in cotton, conventional tillage (CT) and no-tillage (NT). The plant parameters considered for the comparison are: canopy height (CH), canopy cover (CC), canopy volume (CV) and Normalized Difference Vegetation Index (NDVI). Initially, the whole study area was divided into approximately one square meter size grids. Measurements were extracted grid wise using high resolution UAS data captured ten times over whole crop growing season of the cotton. One tailed Z-test hypothesis reveals that there is a significant difference between cotton growth under CT and NT for almost all the epochs. With 95% confidence interval, the crop grown under NT found to have taller canopy, higher canopy cover, bigger biomass and higher NDVI, as compared to those under CT cropping system.
       
  • Remote sensing image fusion via compressive sensing
    • Abstract: Publication date: June 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 152Author(s): Morteza Ghahremani, Yonghuai Liu, Peter Yuen, Ardhendu Behera In this paper, we propose a compressive sensing-based method to pan-sharpen the low-resolution multispectral (LRM) data, with the help of high-resolution panchromatic (HRP) data. In order to successfully implement the compressive sensing theory in pan-sharpening, two requirements should be satisfied: (i) forming a comprehensive dictionary in which the estimated coefficient vectors are sparse; and (ii) there is no correlation between the constructed dictionary and the measurement matrix. To fulfill these, we propose two novel strategies. The first is to construct a dictionary that is trained with patches across different image scales. Patches at different scales or equivalently multiscale patches provide texture atoms without requiring any external database or any prior atoms. The redundancy of the dictionary is removed through K-singular value decomposition (K-SVD). Second, we design an iterative l1-l2 minimization algorithm based on alternating direction method of multipliers (ADMM) to seek the sparse coefficient vectors. The proposed algorithm stacks missing high-resolution multispectral (HRM) data with the captured LRM data, so that the latter is used as a constraint for the estimation of the former during the process of seeking the representation coefficients. Three datasets are used to test the performance of the proposed method. A comparative study between the proposed method and several state-of-the-art ones shows its effectiveness in dealing with complex structures of remote sensing imagery.
       
  • Characterization and modeling of power line corridor elements from LiDAR
           point clouds
    • Abstract: Publication date: June 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 152Author(s): Sebastián Ortega, Agustín Trujillo, José Miguel Santana, José Pablo Suárez, Jaisiel Santana As the electric companies need to assure the reliability of their services, power line management gains importance during the last years. Many of them rely on LiDAR scanning of their assets to obtain the status of their power line corridors and determine possible risks. In this paper, a novel sevenfold staged pipeline is introduced to classify pylon and wire points and model the conductors. Wire points are subdivided into three categories: shield, common conductor and chain. Pylons of two different types are taken into account: suspension and anchor. For the first case, insulator strains are also identified and separated. Wire points are segmented as individual conductors and a 3D-wise model based on the catenary equation is generated for each conductor using particle swarm optimization. Tests have been conducted on a set with 25 point cloud files to assess the accuracy and correctness of the results given by the proposed pipeline.Graphical abstractGraphical abstract for this article
       
  • An improved algorithm for estimating the Secchi disk depth from remote
           sensing data based on the new underwater visibility theory
    • Abstract: Publication date: June 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 152Author(s): Dalin Jiang, Bunkei Matsushita, Fajar Setiawan, Augusto Vundo The Secchi disk depth (ZSD) is a widely used parameter for evaluating water clarity. Here we propose an improved algorithm, which is based on a new underwater visibility theory, for retrieving more accurate ZSD from remote sensing reflectance (Rrs) in various waters. Two improvements were carried out in the new algorithm. First, we used a hybrid quasi-analytical algorithm (QAA_hybrid) instead of the sixth version of QAA (QAA_v6) for retrieving more accurate total absorption coefficient (aλ) and total backscattering coefficient (bbλ) even in turbid inland waters. Second, we used a dynamic KT/Kd ratio (i.e., ratio of diffuse attenuation coefficient of upwelling radiance and diffuse attenuation coefficient of downwelling irradiance) instead of using the fixed ratio (i.e., 1.5). The results obtained from in situ Rrs show that the improved ZSD estimation algorithm gave more accurate ZSD estimations, with the root mean square error (RMSE) reduced from 0.2 to 0.1 in log10 unit, mean absolute percentage error (MAPE) reduced from 39% to 20% (N = 178 with in situ ZSD values between 0.3 and 20.8 m). We then applied the improved ZSD estimation algorithm to the 2003–2012 MERIS images for Lake Kasumigaura to further confirm the performance of the improved ZSD estimation algorithm. The results obtained from 19 matchups demonstrate that the estimated ZSD matched well with the in situ ZSD, with the RMSE of 0.11 m and the MAPE of 15%. The improved ZSD estimation algorithm shows a potential to estimate more accurate ZSD values from remote sensing data in various waters.
       
  • Deep built-structure counting in satellite imagery using attention based
           re-weighting
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Anza Shakeel, Waqas Sultani, Mohsen Ali In this paper, we attempt to address the challenging problem of counting built-structures in the satellite imagery. Building density is a more accurate estimate of the population density, urban area expansion and its impact on the environment, than the built-up area segmentation. However, building shape variances, overlapping boundaries, and variant densities make this a complex task. To tackle this difficult problem, we propose a deep learning based regression technique for counting built-structures in satellite imagery. Our proposed framework intelligently combines features from different regions of satellite image using attention based re-weighting techniques. Multiple parallel convolutional networks are designed to capture information at different granulates. These features are combined into the FusionNet which is trained to weigh features from different granularity differently, allowing us to predict a precise building count. To train and evaluate the proposed method, we put forward a new large-scale and challenging built-structure-count dataset. Our dataset is constructed by collecting satellite imagery from diverse geographical areas (planes, urban centers, deserts, etc.,) across the globe (Asia, Europe, North America, and Africa) and captures the wide density of built structures. Detailed experimental results and analysis validate the proposed technique. FusionNet has Mean Absolute Error of 3.65 and R-squared measure of 88% over the testing data. Finally, we perform the test on the 274.3×103 m2 of the unseen region, with the error of 19 buildings off the 656 buildings in that area.
       
  • A new method of equiangular sectorial voxelization of single-scan
           terrestrial laser scanning data and its applications in forest defoliation
           estimation
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Langning Huo, Xiaoli Zhang Voxelization is an efficient and frequently used data process that is applied to terrestrial laser scanning (TLS) data to facilitate data management and reduce storage size. In this study, an innovative method of equiangular sectorial voxelization is presented based on the distinctive point distribution characteristic of single-scan TLS. It has the function of containing the same number of laser beams going through each voxel, which results in metrics that can be applied to delineate forest conditions. To verify the effectiveness of the new voxelization method and to illustrate its application, 48 plots and 1098 individual trees with different degrees of defoliation were scanned using single-scan TLS. Their defoliation could be linearly regressed by using only point density metrics derived from this new shape of voxels. A 0.89 R2 value and a 12 RMSE (% of defoliation) were obtained for individual-tree-scale estimation, and a 0.83 R2 value and a 12 RMSE (% of defoliation) were obtained for plot-scale estimation. We conclude that the new voxelization method was effective, and the point density that was thus calculated was an efficient feature that revealed forest attributes.
       
  • Filter tensor analysis: A tool for multi-temporal remote sensing target
           detection
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Xiurui Geng, Luyan Ji, Yongchao Zhao With the development of remote sensing technology, more and more multi-temporal multispectral imagery becomes easily available, thus the research of target detection for this type of data will become indispensable. However, the traditional technology of target detection is generally designed for the single-temporal data. In this paper, we introduce the multilinear function as a mathematical tool to deal with the multi-temporal target detection problem for the first time. For an M time phases multispectral data set, we design an Mth-order tensor filter, which corresponds to an M-linear function, to minimize the filter output energy while keeping the target output value invariant, named filter tensor analysis (FTA). Experiments using Landsat time series with two temporally changed targets (i.e. farmland and airport) and two temporally constant targets (i.e. roof and reservoir) all show the effectiveness of FTA for multi-temporal target detection under several commonly used evaluation indices.
       
  • Estimation of the forest stand mean height and aboveground biomass in
           Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM
           imagery
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Yanan Liu, Weishu Gong, Yanqiu Xing, Xiangyun Hu, Jianya Gong Accurate mapping the forest stand mean height (FSMH) and aboveground biomass (AGB) with a high spatial resolution are important for monitoring carbon stocks on Earth and the variability and trends of terrestrial carbon fluxes. The recently launched Sentinel-1 (SAR) and Sentinel-2 (multispectral) missions offers a new opportunity to map FSMH and AGB. Here we present a methodological framework to map the FSMH and AGB at a resolution of 10 m in Yichun, Northeast China, by integrating field plots, Sentinel imagery, topographic data, and national geographical conditions monitoring data. First, a spatial continuous FSMH product was retrieved using an empirical model, which adopts the backscattering of SAR Sentinel-1B and the fraction of vegetation cover (FVC) variable from multispectral Sentinel-2A imagery. Subsequently, three AGB estimation models were developed for different forest types to link the field measurements to the FSMH, biophysical variables, spectral vegetation index, and topographic variables using the random forest algorithm. The mapping results show that the FSMH estimated using SAR backscatter values from VH polarization is more robust and accurate than that based on VV polarization. Furthermore, the three AGB estimation models based on three different forest types perform better than the model built by grouping all forest types together. The determination coefficient (R2) and root-mean-squared error (RMSE) range from 0.69 to 0.74 and 23.38 Mg/ha to 24.21 Mg/ha, respectively. Overall, our study demonstrates that the proposed methodological framework can be used to map the FSMH and AGB products at a high spatial resolution utilizing freely accessible Sentinel-1 SAR and Sentinel-2 multispectral imagery.
       
  • A spatially structured adaptive two-stage model for retrieving
           ground-level PM2.5 concentrations from VIIRS AOD in China
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Fei Yao, Jiansheng Wu, Weifeng Li, Jian Peng While the aerosol optical depth (AOD) product from the Visible Infrared Imaging Suite (VIIRS) instrument has proven effective for estimating regional ground-level particle concentrations with aerodynamic diameters less than 2.5 μm (PM2.5), its performance at larger spatial scales remains unclear. Despite the wide application of statistical models in building ground-level PM2.5 satellite remote sensing retrieval models, a limited number of studies have considered the spatiotemporal heterogeneities for model structures. Taking China as the study area, we used the VIIRS AOD, together with multi-source auxiliary variables, to develop a spatially structured adaptive two-stage model to estimate ground-level PM2.5 concentrations at a 6-km spatial resolution. To this end, we first defined and calculated a dual distance from the ground-level PM2.5 monitoring data. We then applied the unweighted pair-group method with arithmetic means on dual distances and obtained 13 spatial clusters. Subsequently, we combined the time fixed effects regression (TEFR) model and geographically weighted regression (GWR) model to develop the spatially structured adaptive two-stage model. For each spatial cluster, we examined all possible combinations of auxiliary variables and determined the best model structure according to multiple statistical test results. Finally, we obtained the PM2.5 estimates through regression mapping. At least seven model-fitting data records per day made a good threshold that could best overcome the model overfitting induced by the second-stage GWR model at the minimum price of losing samples. The overall model fitting and ten-fold cross validation (CV) R2 were 0.82 and 0.60, respectively, under that threshold. Model performances among different spatial clusters differed to a certain extent. High-CV R2 values always exceeded 0.6 while low-CV R2 values less than 0.5 also existed. Both the size of the model-fitting data records and the extent of urban-industrial characteristics of spatial clusters accounted for these differences. The PM2.5 estimates agreed well with the PM2.5 observations with correlation coefficients all exceeding 0.5 at the monthly, seasonal, and annual scales. East of Hu’s line and north of the Yangtze River were characterized by high PM2.5 concentrations. This study contributes to the understanding of how well VIIRS AOD can retrieve ground-level PM2.5 concentrations at the national scale and strategies for building ground-level PM2.5 satellite remote sensing retrieval models.Graphical abstractGraphical abstract for this article
       
  • Automatic reconstruction of fully volumetric 3D building models from
           oriented point clouds
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Sebastian Ochmann, Richard Vock, Reinhard Klein We present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds with oriented normals by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds.Graphical abstractGraphical abstract for this article
       
  • Exploring semantic elements for urban scene recognition: Deep integration
           of high-resolution imagery and OpenStreetMap (OSM)
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Wenzhi Zhao, Yanchen Bo, Jiage Chen, Dirk Tiede, Blaschke Thomas, William J. Emery Urban scenes refer to city blocks which are basic units of megacities, they play an important role in citizens’ welfare and city management. Remote sensing imagery with largescale coverage and accurate target descriptions, has been regarded as an ideal solution for monitoring the urban environment. However, due to the heterogeneity of remote sensing images, it is difficult to access their geographical content at the object level, let alone understanding urban scenes at the block level. Recently, deep learning-based strategies have been applied to interpret urban scenes with remarkable accuracies. However, the deep neural networks require a substantial number of training samples which are hard to satisfy, especially for high-resolution images. Meanwhile, the crowed-sourced Open Street Map (OSM) data provides rich annotation information about the urban targets but may encounter the problem of insufficient sampling (limited by the places where people can go). As a result, the combination of OSM and remote sensing images for efficient urban scene recognition is urgently needed. In this paper, we present a novel strategy to transfer existing OSM data to high-resolution images for semantic element determination and urban scene understanding. To be specific, the object-based convolutional neural network (OCNN) can be utilized for geographical object detection by feeding it rich semantic elements derived from OSM data. Then, geographical objects are further delineated into their functional labels by integrating points of interest (POIs), which contain rich semantic terms, such as commercial or educational labels. Lastly, the categories of urban scenes are easily acquired from the semantic objects inside. Experimental results indicate that the proposed method has an ability to classify complex urban scenes. The classification accuracies of the Beijing dataset are as high as 91% at the object-level and 88% at the scene level. Additionally, we are probably the first to investigate the object level semantic mapping by incorporating high-resolution images and OSM data of urban areas. Consequently, the method presented is effective in delineating urban scenes that could further boost urban environment monitoring and planning with high-resolution images.
       
  • A new fully convolutional neural network for semantic segmentation of
           polarimetric SAR imagery in complex land cover ecosystem
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Fariba Mohammadimanesh, Bahram Salehi, Masoud Mahdianpari, Eric Gill, Matthieu Molinier Despite the application of state-of-the-art fully Convolutional Neural Networks (CNNs) for semantic segmentation of very high-resolution optical imagery, their capacity has not yet been thoroughly examined for the classification of Synthetic Aperture Radar (SAR) images. The presence of speckle noise, the absence of efficient feature expression, and the limited availability of labelled SAR samples have hindered the application of the state-of-the-art CNNs for the classification of SAR imagery. This is of great concern for mapping complex land cover ecosystems, such as wetlands, where backscattering/spectrally similar signatures of land cover units further complicate the matter. Accordingly, we propose a new Fully Convolutional Network (FCN) architecture that can be trained in an end-to-end scheme and is specifically designed for the classification of wetland complexes using polarimetric SAR (PolSAR) imagery. The proposed architecture follows an encoder-decoder paradigm, wherein the input data are fed into a stack of convolutional filters (encoder) to extract high-level abstract features and a stack of transposed convolutional filters (decoder) to gradually up-sample the low resolution output to the spatial resolution of the original input image. The proposed network also benefits from recent advances in CNN designs, namely the addition of inception modules and skip connections with residual units. The former component improves multi-scale inference and enriches contextual information, while the latter contributes to the recovery of more detailed information and simplifies optimization. Moreover, an in-depth investigation of the learned features via opening the black box demonstrates that convolutional filters extract discriminative polarimetric features, thus mitigating the limitation of the feature engineering design in PolSAR image processing. Experimental results from full polarimetric RADARSAT-2 imagery illustrate that the proposed network outperforms the conventional random forest classifier and the state-of-the-art FCNs, such as FCN-32s, FCN-16s, FCN-8s, and SegNet, both visually and numerically for wetland mapping.Graphical abstractGraphical abstract for this article
       
  • Pipeline leakage detection for district heating systems using multisource
           data in mid- and high-latitude regions
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Yanfei Zhong, Yao Xu, Xinyu Wang, Tianyi Jia, Guisong Xia, Ailong Ma, Liangpei Zhang In mid- and high-latitude regions, district heating systems (DHSs) are major heat supply solutions to both local industry and citizens. Pipeline leakage detection is therefore important for monitoring the condition of DHSs and promoting energy efficiency. In this paper, a saliency analysis method is presented for DHS pipeline leakage detection using remotely sensed infrared imagery, visible imagery, and geographic information system (GIS) data. In the saliency-based DHS leakage detection method, the infrared saliency map is created to enhance the leakage targets, and the pipeline location information extracted from the GIS data or the visible imagery acts as a distribution prior to reject false alarms. Finally, adaptive target segmentation by maximum entropy permits the automatic detection of potential leakage targets in the final fused saliency map. The approach was validated on three data sets acquired in Gävle in Sweden and Datong in China, with the heating leakages indicated by human analysts and field validation. The leakage detection accuracy of the new approach with a reduced false alarm rate is better than the previous methods. The results suggest that the proposed approach for DHS leakage detection from remotely sensed thermal infrared data has great potential for monitoring DHS conditions in mid- and high-latitude regions.
       
  • Balancing prediction accuracy and generalization ability: A hybrid
           framework for modelling the annual dynamics of satellite-derived land
           surface temperatures
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Zihan Liu, Wenfeng Zhan, Jiameng Lai, Falu Hong, Jinling Quan, Benjamin Bechtel, Fan Huang, Zhaoxu Zou Annual temperature cycle (ATC) models enable the multi-timescale analysis of land surface temperature (LST) dynamics and are therefore valuable for various applications. However, the currently available ATC models focus either on prediction accuracy or on generalization ability and a flexible ATC modelling framework for different numbers of thermal observations is lacking. Here, we propose a hybrid ATC model (ATCH) that considers both prediction accuracy and generalization ability; our approach combines multiple harmonics with a linear function of LST-related factors, including surface air temperature (SAT), NDVI, albedo, soil moisture, and relative humidity. Based on the proposed ATCH, various parameter-reduction approaches (PRAs) are designed to provide model derivatives which can be adapted to different scenarios. Using Terra/MODIS daily LST products as evaluation data, the ATCH is compared with the original sinusoidal ATC model (termed the ATCO) and its variants, and with two frequently-used gap-filling methods (Regression Kriging Interpolation (RKI) and the Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST)), under clear-sky conditions. In addition, under overcast conditions, the LSTs generated by ATCH are directly compared with in-situ LST measurements. The comparisons demonstrate that the ATCH increases the prediction accuracy and the overall RMSE is reduced by 1.8 and 0.7 K when compared with the ATCO during daytime and nighttime, respectively. Moreover, the ATCH shows better generalization ability than the RKI and behaves better than the RSDAST when the LST gap size is spatially large and/or temporally long. By employing LST-related controls (e.g., the SAT and relative humidity) under overcast conditions, the ATCH can better predict the LSTs under clouds than approaches that only adopt clear-sky information as model inputs. Further attribution analysis implies that incorporating a sinusoidal function (ASF), the SAT, NDVI, and other LST-related factors, provides respective contributions of around 16%, 40%, 15%, and 30% to the improved accuracy. Our analysis is potentially useful for designing PRAs for various practical needs, by reducing the smallest contribution factor each time. We conclude that the ATCH is valuable for further improving the quality of LST products and can potentially enhance the time series analysis of land surfaces and other applications.
       
  • Fusion of thermal imagery with point clouds for building façade
           thermal attribute mapping
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Dong Lin, Malgorzata Jarzabek-Rychard, Xiaochong Tong, Hans-Gerd Maas Thermal image data are widely used to assess the insulation quality of buildings and to detect thermal leakages. In our approach, we merge terrestrial thermal image data and 3D point clouds to perform thermal texture mapping for building facades. Since geo-referencing data of a hand-held thermal camera is usually not available in such applications, registration between thermal images and a 3D point cloud (for instance generated from RGB image data by structure-from-motion techniques) is essential. In our approach, thermal image data registration is conducted in four steps: First, another point cloud is generated from the thermal image data. Next, a coarse registration between thermal point cloud and RGB point cloud is performed using the fast global registration (FGR) algorithm. The best corresponding thermal-RGB image pairs are acquired by picking up the lowest Euclidean distance between the exterior orientation parameters of thermal images and transformed exterior orientation parameters of RGB images. Subsequently, radiation-invariant feature transform (RIFT), normalized barycentric coordinate system (NBCS) and random sample consensus (RANSAC) are employed to extract reliable matching features on thermal-RGB image pairs. Afterwards, a fine registration is performed by mono-plotting of the RGB image, followed by image resection of the thermal image. Finally, in terms of texture mapping algorithms, in order to remove the blur effects caused by small misalignments for different candidate images, a global image pose refinement approach, which aims to minimize the temperature disagreements provided by different images for the same object points, is proposed. In addition, in order to ensure high geometric and radiant accuracy, camera calibrations are performed. Experiments showed that the proposed method could not only achieve high geometric registration accuracy, but also provide a good radiometric accuracy with RMSE lower than 1.5 °C.
       
  • Planar surface detection for sparse and heterogeneous mobile laser
           scanning point clouds
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Hoang Long Nguyen, David Belton, Petra Helmholz Plane detection and segmentation is one of the most crucial tasks in point cloud processing. The output from this process can be used as input for further processing steps, such as modelling, registration and calibration. However, the sparseness and heterogeneity of Mobile Laser Scanning (MLS) point clouds may lead to problems for existing planar surfaces detection and segmentation methods. This paper proposes a new method that can be applicable to detect and segment planar features in sparse and heterogeneous MLS point clouds. This method utilises the scan profile patterns and the planarity values between different neighbouring scan profiles to detect and segment planar surfaces from MLS point clouds. The proposed method is compared to the three most state-of-the-art segmentation methods (e.g. RANSAC, a robust segmentation method based on robust statistics and diagnostic principal component analysis – RDCPA as well as the plane detection method based on line arrangement). Three datasets are used for the validation of the results. The results show that our proposed method outperforms the existing methods in detecting and segmenting planar surfaces in sparse and heterogeneous MLS point clouds. In some instances, the state-of-the-art methods produce incorrect segmentation results for façade details which have a similar orientation, such as for windows and doors within a façade. While RDCPA produces up to 50% of outliers depending on the neighbourhood threshold, another method could not detect such features at all. When dealing with small features such as a target, some algorithms (including RANSAC) were unable to perform segmentation. However, the propose algorithm was demonstrated to detect all planes in the test data sets correctly. The paper shows that these mis-segmentations in other algorithms may lead to significant errors in the registration process of between 1.047 and 1.614 degrees in the angular parameters, whereas the propose method had only resulted in 0.462 degree angular bias. Furthermore, it is not sensitive to the required method parameters as well as the point density of the point clouds.
       
  • Automatic building extraction from high-resolution aerial images and LiDAR
           data using gated residual refinement network
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Jianfeng Huang, Xinchang Zhang, Qinchuan Xin, Ying Sun, Pengcheng Zhang Automated extraction of buildings from remotely sensed data is important for a wide range of applications but challenging due to difficulties in extracting semantic features from complex scenes like urban areas. The recently developed fully convolutional neural networks (FCNs) have shown to perform well on urban object extraction because of the outstanding feature learning and end-to-end pixel labeling abilities. The commonly used feature fusion or skip-connection refine modules of FCNs often overlook the problem of feature selection and could reduce the learning efficiency of the networks. In this paper, we develop an end-to-end trainable gated residual refinement network (GRRNet) that fuses high-resolution aerial images and LiDAR point clouds for building extraction. The modified residual learning network is applied as the encoder part of GRRNet to learn multi-level features from the fusion data and a gated feature labeling (GFL) unit is introduced to reduce unnecessary feature transmission and refine classification results. The proposed model - GRRNet is tested in a publicly available dataset with urban and suburban scenes. Comparison results illustrated that GRRNet has competitive building extraction performance in comparison with other approaches. The source code of the developed GRRNet is made publicly available for studies.
       
  • Remote sensing-based crop lodging assessment: Current status and
           perspectives
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Sugandh Chauhan, Roshanak Darvishzadeh, Mirco Boschetti, Monica Pepe, Andrew Nelson Rapid and quantitative assessment of crop lodging is important for understanding the causes of the phenomena, improving crop management, making better production and supporting loss estimates in general. Accurate information on the location and timing of crop lodging is valuable for farmers, agronomists, insurance loss adjusters, and policymakers. Lodging studies can be performed to assess the impact of lodging events or to model the risk of occurrence, both of which rely on information that can be acquired by field observations, from meteorological data and from remote sensing (RS). While studies applying RS data to assess crop lodging dates back three decades, there has been no comprehensive review of the status, potential, current approaches, and challenges in this domain. In this position paper, we review the trends in field/lab-based and RS-based studies for crop lodging assessment and discuss the strengths and weaknesses of current approaches. Theoretical background on crop lodging is presented, and the scope of RS in assessing plant characteristics associated with lodging is reviewed and discussed. The review focuses on RS-based studies, grouping them according to the platform deployed (i.e., ground-based, airborne and spaceborne), with an emphasis on analyzing the pros and cons of the technology. Finally, the challenges, research gaps, perspectives for future research, and an outlook on new sensors and platforms are presented to provide state-of-the-art and future scenarios of RS in lodging assessment. Our review reveals that the use of RS techniques in crop lodging assessment is still in an experimental stage. However, there is increasing interest within the RS scientific community (based on the increased rate of publications over time) to investigate its use for crop lodging detection and risk mapping. The existing satellite-based lodging assessment studies are very few, and the operational application of the current approaches over large spatial extents seems to be the biggest challenge. We identify opportunities for future studies that can develop quantitative models for estimating lodging severity and mapping lodging risk using RS data.
       
  • Pairwise coarse registration of point clouds in urban scenes using
           voxel-based 4-planes congruent sets
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Yusheng Xu, Richard Boerner, Wei Yao, Ludwig Hoegner, Uwe Stilla To ensure complete coverage when measuring a large-scale urban area, pairwise registration between point clouds acquired via terrestrial laser scanning or stereo image matching is usually necessary when there is insufficient georeferencing information from additional GNSS and INS sensors. In this paper, we propose a semi-automatic and target-less method for coarse registration of point clouds using geometric constraints of voxel-based 4-plane congruent sets (V4PCS). The planar patches are firstly extracted from voxelized point clouds. Then, the transformation invariant, 4-plane congruent sets are constructed from extracted planar surfaces in each point cloud. Initial transformation parameters between point clouds are estimated via corresponding congruent sets having the highest registration scores in the RANSAC process. Finally, a closed-form solution is performed to achieve optimized transformation parameters by finding all corresponding planar patches using the initial transformation parameters. Experimental results reveal that our proposed method can be effective for registering point clouds acquired from various scenes. A success rate of better than 80% was achieved, with average rotation errors of about 0.5 degrees and average translation errors less than approximately 0.6 m. In addition, our proposed method is more efficient than other baseline methods when using the same hardware and software configuration conditions.
       
  • Detecting and characterizing downed dead wood using terrestrial laser
           scanning
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Tuomas Yrttimaa, Ninni Saarinen, Ville Luoma, Topi Tanhuanpää, Ville Kankare, Xinlian Liang, Juha Hyyppä, Markus Holopainen, Mikko Vastaranta Dead wood is a key forest structural component for maintaining biodiversity and storing carbon. Despite its important role in a forest ecosystem, quantifying dead wood alongside standing trees has often neglected when investigating the feasibility of terrestrial laser scanning (TLS) in forest inventories. The objective of this study was therefore to develop an automatic method for detecting and characterizing downed dead wood with a diameter exceeding 5 cm using multi-scan TLS data. The developed four-stage algorithm included (1) RANSAC-cylinder filtering, (2) point cloud rasterization, (3) raster image segmentation, and (4) dead wood trunk positioning. For each detected trunk, geometry-related quality attributes such as dimensions and volume were automatically determined from the point cloud. For method development and validation, reference data were collected from 20 sample plots representing diverse southern boreal forest conditions. Using the developed method, the downed dead wood trunks were detected with an overall completeness of 33% and correctness of 76%. Up to 92% of the downed dead wood volume were detected at plot level with mean value of 68%. We were able to improve the detection accuracy of individual trunks with visual interpretation of the point cloud, in which case the overall completeness was increased to 72% with mean proportion of detected dead wood volume of 83%. Downed dead wood volume was automatically estimated with an RMSE of 15.0 m3/ha (59.3%), which was reduced to 6.4 m3/ha (25.3%) as visual interpretation was utilized to aid the trunk detection. The reliability of TLS-based dead wood mapping was found to increase as the dimensions of dead wood trunks increased. Dense vegetation caused occlusion and reduced the trunk detection accuracy. Therefore, when collecting the data, attention must be paid to the point cloud quality. Nevertheless, the results of this study strengthen the feasibility of TLS-based approaches in mapping biodiversity indicators by demonstrating an improved performance in quantifying ecologically most valuable downed dead wood in diverse forest conditions.Graphical abstractGraphical abstract for this article
       
  • Multi-temporal image change mining based on evidential conflict reasoning
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Fatma Haouas, Basel Solaiman, Zouhour Ben Dhiaf, Atef Hamouda, Khaled Bsaies Change detection monitoring on multi-temporal remote sensed images is a persistent methodological challenge where the Dempster-Shafer, or evidence, Theory (DST) has been often applied. This paper presents a new method based on the use of DST for mining bi-temporal remotely sensed images change. The main idea is based on the investigation, analysis and interpretation of different types of conflict between two bi-temporal mass distributions. The reasoning process is focused on the conflict significance and its “partial” causes. In fact, the global conflict that occurs during the joint exploitation of multi-temporal images gives general and non-sufficiently concise information. However, the partial conflict provides rich and important information with regards to the disagreement between knowledge sources. For computing the partial conflict between focal elements, the geometric representation of mass distributions is exploited. The obtained conflict measures, caused by change, are analyzed latter by a new algorithm for drifting binary change map and identifying change directions. The effectiveness and reliability of the proposed approach are shown through experimentations on simulated changed images as well as using multi-temporal Landsat satellite images where qualitative criteria as well as quantitative measures are applied. The performances of the proposed approach, in terms of changed area recognition, are compared to three different and widely used conflict measures: the Empty-set mass, the Jousselme’s distance and the Cosine measure. It is shown that the developed change detection approach outperforms these conflict measures.
       
  • Virtual Support Vector Machines with self-learning strategy for
           classification of multispectral remote sensing imagery
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Christian Geiß, Patrick Aravena Pelizari, Lukas Blickensdörfer, Hannes Taubenböck We follow the idea of learning invariant decision functions for remote sensing image classification with Support Vector Machines (SVM). To do so, we generate artificially transformed samples (i.e., virtual samples) from available prior knowledge. Labeled samples closest to the separating hyperplane with maximum margin (i.e., the Support Vectors) are identified by learning an initial SVM model. The Support Vectors are used for generating virtual samples by perturbing the features to which the model should be invariant. Subsequently, the model is relearned using the Support Vectors and the virtual samples to eventually alter the hyperplane with maximum margin and enhance generalization capabilities of decision functions. In contrast to existing approaches, we establish a self-learning procedure to ultimately prune non-informative virtual samples from a possibly arbitrary invariance generation process to allow for robust and sparse model solutions. The self-learning strategy jointly considers a similarity and margin sampling constraint. In addition, we innovatively explore the invariance generation process in the context of an object-based image analysis framework. Image elements (i.e., pixels) are aggregated to image objects (as represented by segments/superpixels) with a segmentation algorithm. From an initial singular segmentation level, invariances are encoded by varying hyperparameters of the segmentation algorithm in terms of scale and shape. Experimental results are obtained from two very high spatial resolution multispectral data sets acquired over the city of Cologne, Germany, and the Hagadera Refugee Camp, Kenya. Comparative model accuracy evaluations underline the favorable performance properties of the proposed methods especially in settings with very few labeled samples.
       
  • Vegetation Index Weighted Canopy Volume Model (CVMVI) for soybean biomass
           estimation from Unmanned Aerial System-based RGB imagery
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Maitiniyazi Maimaitijiang, Vasit Sagan, Paheding Sidike, Matthew Maimaitiyiming, Sean Hartling, Kyle T. Peterson, Michael J.W. Maw, Nadia Shakoor, Todd Mockler, Felix B. Fritschi Crop biomass estimation with high accuracy at low-cost is valuable for precision agriculture and high-throughput phenotyping. Recent technological advances in Unmanned Aerial Systems (UAS) significantly facilitate data acquisition at low-cost along with high spatial, spectral, and temporal resolution. The objective of this study was to explore the potential of UAS RGB imagery-derived spectral, structural, and volumetric information, as well as a proposed vegetation index weighted canopy volume model (CVMVI) for soybean [Glycine max (L.) Merr.] aboveground biomass (AGB) estimation. RGB images were collected from low-cost UAS throughout the growing season at a field site near Columbia, Missouri, USA. High-density point clouds were produced using the structure from motion (SfM) technique through a photogrammetric workflow based on UAS stereo images. Two-dimensional (2D) canopy structure metrics such as canopy height (CH) and canopy projected basal area (BA), as well as three-dimensional (3D) volumetric metrics such as canopy volume model (CVM) were derived from photogrammetric point clouds. A variety of vegetation indices (VIs) were also extracted from RGB orthomosaics. Then, CVMVI, which combines canopy spectral and volumetric information, was proposed. Commonly used regression models were established based on the UAS-derived information and field- measured AGB with a leave-one-out cross-validation. The results show that: (1) In general, canopy 2D structural metrics CH and BA yielded higher correlation with AGB than VIs. (2) Three-dimensional metrics, such as CVM, that encompass both horizontal and vertical properties of canopy provided better estimates for AGB compared to 2D structural metrics (R2 = 0.849; RRMSE = 18.7%; MPSE = 20.8%). (3) Optimized CVMVI, which incorporates both canopy spectral and 3D volumetric information outperformed the other indices and metrics, and was a better predictor for AGB estimation (R2 = 0.893; RRMSE = 16.3%; MPSE = 19.5%). In addition, CVMVI showed equal prediction power for different genotypes, which indicates its potential for high-throughput soybean biomass estimation. Moreover, a CVMVI based univariate regression model yielded AGB predicting capability comparable to multivariate complex regression models such as stepwise multilinear regression (SMR) and partial least squares regression (PLSR) that incorporate multiple canopy spectral indices and structural metrics. Overall, this study reveals the potential of canopy spectral, structural and volumetric information, and their combination (i.e., CVMVI) for estimations of soybean AGB. CVMVI was shown to be simple but effective in estimating AGB, and could be applied for high-throughput phenotyping and precision agro-ecological applications and management.
       
  • Robust registration for remote sensing images by combining and localizing
           feature- and area-based methods
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Ruitao Feng, Qingyun Du, Xinghua Li, Huanfeng Shen Highly accurate registration is one of the essential requirements for numerous applications of remote sensing images. Toward this end, we have developed a robust algorithm by combining and localizing feature- and area-based methods. A block-weighted projective (BWP) transformation model is first employed to map the local geometric relationship with weighted feature points in the feature-based stage, for which the weight is determined by an inverse distance weighted (IDW) function. Subsequently, the outlier-insensitive (OIS) model aims to further optimize the registration in the area-based stage. Considering the inevitable outliers (e.g., cloud, noise, land-cover change), OIS integrates Huber estimation with the structure tensor (ST), which is an approach that is robust to residual errors and outliers while preserving edges. Four pairs of remote sensing images with varied terrain features were tested in the experiments. Compared with the-state-of-art algorithms, the proposed algorithm is more effective, in terms of both visual quality and quantitative evaluation.
       
  • Corrigendum to “Computing multiple aggregation levels and contextual
           features for road facilities recognition using mobile laser scanning
           data” [ISPRS J. Photogram. Rem. Sens. 126 (2017) 180–194]
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Bisheng Yang, Zhen Dong, Yuan Liu, Fuxun Liang, Yongjun Wang
       
  • A dual band algorithm for shallow water depth retrieval from high spatial
           resolution imagery with no ground truth
    • Abstract: Publication date: May 2019Source: ISPRS Journal of Photogrammetry and Remote Sensing, Volume 151Author(s): Benqing Chen, Yanming Yang, Dewei Xu, Erhui Huang For shallow water depth retrieval from high spatial resolution satellite images, although numerous empirical models have been developed, it remains impossible to estimate shallow water depths without collection of required ground truth depth. To address this limitation, a new physically based dual band algorithm is developed to estimate shallow water depths using blue and green bands from high spatial resolution multispectral image with no ground truth. The dual band log-linear model is first analytically formulated, which then is used for shallow water depths retrieval by solving all unknown model parameters based on different types of sampling pixels directly extracted from the multispectral image. The adjacent pixel pairs from the intersecting edges of different bottom types across various depths over shallow water area, are employed to calculate the optimal band rotation coefficient unit vector by minimization method. On the basis, the bottom parameter is estimated through the pixels from the coastline. Additionally, the pixels from various depths of same bottom type are also employed to achieve the blue to green band ratio of diffused attenuation coefficient. The sum of the diffuse attenuation coefficients of green band for upwelling and downwelling light is estimated by QAA and Kd algorithms. To evaluate the performance of the proposed algorithm, the GeoEye-1 image covered Jinqing Island and the Chinese Gaofen-2 image across Kaneohe Bay are chosen to achieve shallow water depth by using the proposed algorithm after geo-rectification and atmospheric correction. The validations using the actual water depths show the overall root mean square errors (RMSEs) for the derived water depths are 1.18 m for Jinqing Island and 1.34 m for Kaneohe Bay respectively. Compared to the Lyzenga empirical model, the developed approach can generally achieve slightly better results for shallow water depths with no ground truth data. Finally, the effects of the variation in the model parameters to water depth retrieval are discussed and analyzed.
       
 
 
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