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  Subjects -> ELECTRONICS (Total: 156 journals)
Showing 1 - 200 of 277 Journals sorted alphabetically
Advances in Biosensors and Bioelectronics     Open Access   (Followers: 3)
Advances in Electronics     Open Access   (Followers: 6)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 6)
Advances in Microelectronic Engineering     Open Access   (Followers: 7)
Advances in Power Electronics     Open Access   (Followers: 11)
Aerospace and Electronic Systems, IEEE Transactions on     Hybrid Journal   (Followers: 127)
American Journal of Electrical and Electronic Engineering     Open Access   (Followers: 16)
Annals of Telecommunications     Hybrid Journal   (Followers: 6)
APSIPA Transactions on Signal and Information Processing     Open Access   (Followers: 6)
Archives of Electrical Engineering     Open Access   (Followers: 10)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 5)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 18)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 15)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 23)
Biomedical Instrumentation & Technology     Hybrid Journal   (Followers: 6)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 7)
BULLETIN of National Technical University of Ukraine. Series RADIOTECHNIQUE. RADIOAPPARATUS BUILDING     Open Access   (Followers: 1)
Bulletin of the Polish Academy of Sciences : Technical Sciences     Open Access  
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 23)
China Communications     Full-text available via subscription   (Followers: 6)
Circuits and Systems     Open Access   (Followers: 9)
Consumer Electronics Times     Open Access   (Followers: 5)
Control Systems     Hybrid Journal   (Followers: 49)
Edu Elektrika Journal     Open Access  
Electronic Design     Partially Free   (Followers: 31)
Electronic Markets     Hybrid Journal   (Followers: 6)
Electronic Materials Letters     Hybrid Journal   (Followers: 1)
Electronics     Open Access   (Followers: 12)
Electronics and Communications in Japan     Hybrid Journal   (Followers: 5)
Electronics For You     Partially Free   (Followers: 2)
Electronics Letters     Hybrid Journal   (Followers: 19)
Embedded Systems Letters, IEEE     Hybrid Journal   (Followers: 29)
Energy Harvesting and Systems : Materials, Mechanisms, Circuits and Storage     Hybrid Journal   (Followers: 2)
EPJ Quantum Technology     Open Access  
EURASIP Journal on Embedded Systems     Open Access   (Followers: 9)
Facta Universitatis, Series : Electronics and Energetics     Open Access  
Frequenz     Hybrid Journal   (Followers: 1)
Frontiers of Optoelectronics     Hybrid Journal  
Geoscience and Remote Sensing, IEEE Transactions on     Hybrid Journal   (Followers: 30)
Giroskopiya i Navigatsiya     Open Access  
Haptics, IEEE Transactions on     Hybrid Journal   (Followers: 2)
IEEE Antennas and Propagation Magazine     Hybrid Journal   (Followers: 32)
IEEE Antennas and Wireless Propagation Letters     Hybrid Journal   (Followers: 27)
IEEE Journal of Emerging and Selected Topics in Power Electronics     Hybrid Journal   (Followers: 19)
IEEE Journal of the Electron Devices Society     Open Access   (Followers: 5)
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits     Hybrid Journal  
IEEE Power Electronics Magazine     Full-text available via subscription   (Followers: 25)
IEEE Transactions on Antennas and Propagation     Full-text available via subscription   (Followers: 24)
IEEE Transactions on Automatic Control     Hybrid Journal   (Followers: 42)
IEEE Transactions on Circuits and Systems for Video Technology     Hybrid Journal   (Followers: 10)
IEEE Transactions on Consumer Electronics     Hybrid Journal   (Followers: 20)
IEEE Transactions on Electron Devices     Hybrid Journal   (Followers: 10)
IEEE Transactions on Information Theory     Hybrid Journal   (Followers: 15)
IEEE Transactions on Power Electronics     Hybrid Journal   (Followers: 39)
IEEE Transactions on Signal and Information Processing over Networks     Full-text available via subscription   (Followers: 5)
IEICE - Transactions on Electronics     Full-text available via subscription   (Followers: 9)
IEICE - Transactions on Information and Systems     Full-text available via subscription   (Followers: 6)
IET Microwaves, Antennas & Propagation     Hybrid Journal   (Followers: 9)
IET Power Electronics     Hybrid Journal   (Followers: 19)
IET Wireless Sensor Systems     Hybrid Journal   (Followers: 13)
IETE Journal of Education     Open Access   (Followers: 2)
IETE Journal of Research     Open Access   (Followers: 8)
IETE Technical Review     Open Access   (Followers: 6)
Industrial Electronics, IEEE Transactions on     Hybrid Journal   (Followers: 19)
Industry Applications, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Informatik-Spektrum     Hybrid Journal   (Followers: 2)
Instabilities in Silicon Devices     Full-text available via subscription  
Intelligent Transportation Systems Magazine, IEEE     Full-text available via subscription   (Followers: 6)
International Journal of Advanced Research in Computer Science and Electronics Engineering     Open Access   (Followers: 14)
International Journal of Advances in Telecommunications, Electrotechnics, Signals and Systems     Open Access   (Followers: 4)
International Journal of Aerospace Innovations     Full-text available via subscription   (Followers: 13)
International Journal of Antennas and Propagation     Open Access   (Followers: 7)
International Journal of Applied Electronics in Physics & Robotics     Open Access   (Followers: 3)
International Journal of Computational Vision and Robotics     Hybrid Journal   (Followers: 4)
International Journal of Computer & Electronics Research     Full-text available via subscription   (Followers: 1)
International Journal of Control     Hybrid Journal   (Followers: 12)
International Journal of Electronics     Hybrid Journal   (Followers: 1)
International Journal of Electronics & Data Communication     Open Access   (Followers: 5)
International Journal of Electronics and Telecommunications     Open Access   (Followers: 6)
International Journal of Granular Computing, Rough Sets and Intelligent Systems     Hybrid Journal  
International Journal of High Speed Electronics and Systems     Hybrid Journal  
International Journal of Image, Graphics and Signal Processing     Open Access   (Followers: 6)
International Journal of Microwave and Wireless Technologies     Hybrid Journal   (Followers: 2)
International Journal of Nano Devices, Sensors and Systems     Open Access   (Followers: 5)
International Journal of Nanoscience     Hybrid Journal   (Followers: 2)
International Journal of Numerical Modelling:Electronic Networks, Devices and Fields     Hybrid Journal   (Followers: 2)
International Journal of Power Electronics     Hybrid Journal   (Followers: 11)
International Journal of Review in Electronics & Communication Engineering     Open Access   (Followers: 4)
International Journal of Sensors, Wireless Communications and Control     Hybrid Journal   (Followers: 6)
International Journal of Systems, Control and Communications     Hybrid Journal   (Followers: 3)
International Journal of Wireless and Microwave Technologies     Open Access   (Followers: 3)
International Journal on Communication     Full-text available via subscription   (Followers: 12)
International Journal on Electrical and Power Engineering     Full-text available via subscription   (Followers: 7)
International Transaction of Electrical and Computer Engineers System     Open Access   (Followers: 1)
Journal of Biosensors & Bioelectronics     Open Access   (Followers: 3)
Journal of Advanced Dielectrics     Open Access   (Followers: 1)
Journal of Artificial Intelligence     Open Access   (Followers: 6)
Journal of Circuits, Systems, and Computers     Hybrid Journal   (Followers: 2)
Journal of Computational Intelligence and Electronic Systems     Full-text available via subscription  
Journal of Electrical and Electronics Engineering Research     Open Access   (Followers: 9)
Journal of Electrical Bioimpedance     Full-text available via subscription   (Followers: 2)
Journal of Electrical Engineering & Electronic Technology     Hybrid Journal   (Followers: 5)
Journal of Electromagnetic Analysis and Applications     Open Access   (Followers: 5)
Journal of Electromagnetic Waves and Applications     Hybrid Journal   (Followers: 4)
Journal of Electronic Design Technology     Full-text available via subscription   (Followers: 2)
Journal of Electronics (China)     Hybrid Journal   (Followers: 3)
Journal of Field Robotics     Hybrid Journal   (Followers: 2)
Journal of Guidance, Control, and Dynamics     Full-text available via subscription   (Followers: 103)
Journal of Intelligent Procedures in Electrical Technology     Open Access   (Followers: 3)
Journal of Low Power Electronics     Full-text available via subscription   (Followers: 5)
Journal of Low Power Electronics and Applications     Open Access   (Followers: 3)
Journal of Microwaves, Optoelectronics and Electromagnetic Applications     Open Access   (Followers: 7)
Journal of Nuclear Cardiology     Hybrid Journal  
Journal of Optoelectronics Engineering     Open Access   (Followers: 1)
Journal of Physics B: Atomic, Molecular and Optical Physics     Hybrid Journal   (Followers: 7)
Journal of Power Electronics & Power Systems     Full-text available via subscription   (Followers: 6)
Journal of Semiconductors     Full-text available via subscription   (Followers: 2)
Journal of Sensors     Open Access   (Followers: 15)
Journal of Signal and Information Processing     Open Access   (Followers: 7)
Jurnal Infotel     Open Access  
Jurnal Rekayasa Elektrika     Open Access  
Learning Technologies, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Magnetics Letters, IEEE     Hybrid Journal   (Followers: 5)
Metrology and Measurement Systems     Open Access   (Followers: 3)
Microelectronics and Solid State Electronics     Open Access   (Followers: 12)
Nanotechnology Magazine, IEEE     Full-text available via subscription   (Followers: 29)
Nanotechnology, Science and Applications     Open Access   (Followers: 1)
Networks: an International Journal     Hybrid Journal   (Followers: 4)
Open Journal of Antennas and Propagation     Open Access   (Followers: 2)
Optical Communications and Networking, IEEE/OSA Journal of     Full-text available via subscription   (Followers: 6)
Paladyn, Journal of Behavioral Robotics     Open Access  
Progress in Quantum Electronics     Full-text available via subscription   (Followers: 7)
Pulse     Full-text available via subscription   (Followers: 4)
Radiophysics and Quantum Electronics     Hybrid Journal   (Followers: 2)
Recent Patents on Electrical & Electronic Engineering     Full-text available via subscription   (Followers: 5)
Recent Patents on Telecommunications     Full-text available via subscription   (Followers: 2)
Research & Reviews : Journal of Embedded System & Applications     Full-text available via subscription   (Followers: 2)
Security and Communication Networks     Hybrid Journal   (Followers: 2)
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of     Hybrid Journal   (Followers: 21)
Semiconductors and Semimetals     Full-text available via subscription  
Sensing and Imaging : An International Journal     Hybrid Journal   (Followers: 1)
Services Computing, IEEE Transactions on     Hybrid Journal   (Followers: 4)
Software Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 33)
Solid-State Circuits Magazine, IEEE     Hybrid Journal   (Followers: 8)
Solid-State Electronics     Hybrid Journal   (Followers: 5)
Superconductor Science and Technology     Hybrid Journal   (Followers: 1)
Synthesis Lectures on Power Electronics     Full-text available via subscription  
Technical Report Electronics and Computer Engineering     Open Access  
Telematique     Open Access  
TELKOMNIKA (Telecommunication, Computing, Electronics and Control)     Open Access   (Followers: 3)
Universal Journal of Electrical and Electronic Engineering     Open Access   (Followers: 4)
Visión Electrónica : algo más que un estado sólido     Open Access  
Wireless and Mobile Technologies     Open Access   (Followers: 3)
Wireless Power Transfer     Full-text available via subscription   (Followers: 2)
Women in Engineering Magazine, IEEE     Full-text available via subscription   (Followers: 9)
Електротехніка і Електромеханіка     Open Access  

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Journal Cover Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
  [SJR: 1.632]   [H-I: 19]   [21 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1939-1404
   Published by IEEE Homepage  [191 journals]
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing
    • Abstract: Provides a listing of current committee members and society officers.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Institutional listings [Advertisements]
    • Abstract: Advertisement.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote
           Sensing publication information
    • Abstract: Provides a listing of current staff, committee members and society officers.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Front Cover
    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Table of contents
    • Pages: 1761 - 1762
      Abstract: Presents the table of contents for this issue of this publication.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Foreword to the Special Issue on Urban Remote Sensing
    • Pages: 1763 - 1766
      Abstract: The 28 papers in this special issue present a collection of papers focusing on the application of remote sensing to urban areas.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Automatic Building Detection From High-Resolution Satellite Images Based
           on Morphology and Internal Gray Variance
    • Authors: Chaudhuri; D.;Kushwaha, N.K.;Samal, A.;Agarwal, R.C.;
      Pages: 1767 - 1779
      Abstract: Automatic building extraction remains an open research topic in digital photogrammetry and remote sensing. While many algorithms have been proposed for building extraction, none of them solve the problem completely. This is even a greater challenge in urban areas, due to high-object density and scene complexity. Standard approaches do not achieve satisfactory performance, especially with high-resolution satellite images. This paper presents a novel framework for reliable and accurate building extraction from high-resolution panchromatic images. Proposed framework exploits the domain knowledge (spatial and spectral properties) about the nature of objects in the scene, their optical interactions and their impact on the resulting image. The steps in the approach consist of 1) directional morphological enhancement; 2) multiseed-based clustering technique using internal gray variance (IGV); 3) shadow detection; 4) false alarm reduction using positional information of both building edge and shadow; and 5) adaptive threshold based segmentation technique. We have evaluated the algorithm using a variety of images from IKONOS and QuickBird satellites. The results demonstrate that the proposed algorithm is both accurate and efficient.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • A Self-Supervised Decision Fusion Framework for Building Detection
    • Authors: Senaras; C.;Yarman Vural, F.T.;
      Pages: 1780 - 1791
      Abstract: In this study, a new building detection framework for monocular satellite images, called self-supervised decision fusion (SSDF) is proposed. The model is based on the idea of self-supervision, which aims to generate training data automatically from each individual test image, without human interaction. This approach allows us to use the advantages of the supervised classifiers in a fully automated framework. We combine our previous supervised and unsupervised building detection frameworks to suggest a self-supervised learning architecture. Hence, we borrow the major strength of the unsupervised approach to obtain one of the most important clues, the relation of a building, and its cast shadow. This important information is, then, used in order to satisfy the requirement of training sample selection. Finally, an ensemble learning algorithm, called fuzzy stacked generalization (FSG), fuses a set of supervised classifiers trained on the automatically generated dataset with various shape, color, and texture features. We assessed the building detection performance of the proposed approach over 19 test sites and compare our results with the state of the art algorithms. Our experiments show that the supervised building detection method requires more than 30% of the ground truth (GT) training data to reach the performance of the proposed SSDF method. Furthermore, the SSDF method increases the F-score by 2 percentage points (p.p.) on the average compared to performance of the unsupervised method.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Accurate Detection of Anthropogenic Settlements in Hyperspectral Images by
           Higher Order Nonlinear Unmixing
    • Authors: Marinoni; A.;Gamba, P.;
      Pages: 1792 - 1801
      Abstract: In order to achieve a better knowledge of the effect of the anthropogenic extents over the environment, extracting reliable and effective information by Earth observations (EOs) is crucial to help developing a sound human-environment interaction (HEI) assessment. In this sense, the use of future hyperspectral sensors for wide area characterization leads to the need of hyperspectral unmixing (HSU) architectures to recognize urban materials and structures. Further, as urban settlements are often characterized by geometrically and spectrally complex scenarios, the nonlinear reflectance interplay among the elements that constitute each scene must be very well detailed and described so that a thorough knowledge of the scenes can be carried out. In this paper, properly set higher order nonlinear mixture models are used to perform an accurate characterization of the anthropogenic settlements in several EO scenes acquired in different continents. Moreover, a brand new index for estimation of urban extents is provided. Experimental results show how the proposed approach is able to deliver accurate and reliable characterization of urban materials and extents.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Detection of Manhole Covers in High-Resolution Aerial Images of Urban
           Areas by Combining Two Methods
    • Authors: Pasquet; J.;Desert, T.;Bartoli, O.;Chaumont, M.;Delenne, C.;Subsol, G.;Derras, M.;Chahinian, N.;
      Pages: 1802 - 1807
      Abstract: Mispositioning of buried utilities is an increasingly important problem both in industrialized and developing countries because of urban sprawl and technological advances. However, some of these networks have surface access traps, which may be visible on high-resolution airborne or satellite images and could serve as presence indicators. We put forward a methodology to detect manhole covers and grates on very high-resolution aerial and satellite images. Two methods are tested: the first is based on a geometrical circular filter, whereas the second one uses machine learning to retrieve some patterns. The results are compared and combined to benefit from the two approaches.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Developing a Spectral-Based Strategy for Urban Object Detection From
           Airborne Hyperspectral TIR and Visible Data
    • Authors: Eslami; M.;Mohammadzadeh, A.;
      Pages: 1808 - 1816
      Abstract: Classification and detection of urban objects have been big challenges for years. High spatial resolution hyperspectral thermal infrared (HSR-HTIR) is a novel source of data that became available in recent years for urban object detection. In this research, a novel method is proposed for integration of HTIR and very high spatial resolution (VHSR) visible image to classify urban objects. First, atmospheric corrections were enforced to the HSR-HTIR. Second, for the first time, projection pursuit (PP) band reduction method was applied to a novel source of data, and the results achieved are better than those obtained by applying principal component analysis (PCA) as a well-known band reduction approach. Then, various features derived from HSR-HTIR and VHSR images were fed to a pixel-based support vector machine (SVM) classification algorithm, and seven urban classes detected. Afterward, an innovative strategy, using object-rule-based postprocessing approach, was introduced for postclassification of the raw classification results. Finally, a decision-based overlaying process was carried out to produce the final map. The classification results obtained indicate the high potential of using only spectral features. Consequently, its implementation becomes more feasible and the accuracies obtained are competitive in comparison to the results announced previously by the IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion contest 2014.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Information Fusion for Urban Road Extraction From VHR Optical Satellite
           Images
    • Authors: Miao; Z.;Shi, W.;Samat, A.;Lisini, G.;Gamba, P.;
      Pages: 1817 - 1829
      Abstract: This paper presents a novel method exploiting fusion at the information level for urban road extraction from very high resolution (VHR) optical satellite images. Given a satellite image, we explore spectral and shape features computed at the pixel level, and use them to select road segments using two different methods (i.e., expectation maximization clustering and linearness filtering). A road centerline extraction method, which is relying on the outlier robust regression, is subsequently applied to extract accurate centerlines from road segments. After that, three different sets of information fusion rules are applied to jointly exploit results from these methods, which offer ways to address their own limitations. Two VHR optical satellite images are used to validate the proposed method. Quantitative results prove that information fusion following centerline extraction by multiple techniques is able to produce the best accuracy values for automatic urban road extraction from VHR optical satellite images.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Extraction of Slum Areas From VHR Imagery Using GLCM Variance
    • Pages: 1830 - 1840
      Abstract: Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as WorldView-2, OrbView, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized difference vegetation index (NDVI) is able to separate slums and formal areas. The overall accuracy achieved is 84% in Kigali, 87% in Mumbai, and 88% in Ahmedabad. Furthermore, combining spectral information with the GLCM variance within a random forest classifier results in a pixel-based classification accuracy of 90%. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88%-95% for slum locations depending on the scale parameter.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Contributing to WUDAPT: A Local Climate Zone Classification of Two Cities
           in Ukraine
    • Pages: 1841 - 1853
      Abstract: Local climate zones (LCZs) divide the urban landscape into homogeneous types based on urban structure (i.e., morphology of streets and buildings), urban cover (i.e., permeability of surfaces), construction materials, and human activities (i.e., anthropogenic heat). This classification scheme represents a standardized way of capturing the basic urban form of cities and is currently being applied globally as part of the world urban database and portal tools (WUDAPT) initiative. This paper assesses the transferability of the LCZ concept to two Ukrainian cities, i.e., Kyiv and Lviv, which differ in urban form and topography, and considers three ways to validate and verify this classification scheme. An accuracy of 64% was achieved for Kyiv using an independent validation dataset while a comparison of the LCZ maps with the GlobeLand30 land cover map resulted in a match that was greater than 75% for both cities. There was also good correspondence between the urban classes in the LCZ maps and the urban points of interest in OpenStreetMap (OSM). However, further research is still required to produce a standardized validation protocol that could be used on a regular basis by contributors to WUDAPT to help produce more accurate LCZ maps in the future.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • An Extended Random Walker Approach for Object Extraction by Integrating
           VGI Data and VHR Image
    • Pages: 1854 - 1863
      Abstract: Automatic extraction of objects in urban areas from very-high-resolution (VHR) images is of great significance to many applications. Existing approaches consider little information on spatial relationships, backgrounds, and prior knowledge of target objects, leading to that they did not perform well in object extraction. Now free and fast growing volunteered geographic information (VGI) can be accessed easily; thus, they can be used as prior information to improve the performance. This study develops an extended random walker (RW) approach to form a bottom-up and top-down mechanism for extracting target objects by combining VHR images and VGI data. Novel aspects of our approach include: 1) both the shape and spectral prior terms are incorporated into the extended RW algorithm; 2) an end-to-end framework is proposed to automatically select both foreground and background seeds with the assistance of VGI data; and 3) the shape prior of VGI data provides top-down information to select background and foreground seeds and help fuse bottom-up image information (i.e., foreground and background seeds and spatial relationships) to extract target objects. The extended RW approach was validated on building and lake datasets, and its performance is evaluated on both pixel and object levels. Quantitative comparisons with the original RW and random forest (RF) algorithm indicate that the proposed approach achieves significant better performance. Besides, it can successfully extract the partly occluded buildings.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • A Novel Building and Tree Detection Method From LiDAR Data and Aerial
           Images
    • Authors: Zarea; A.;Mohammadzadeh, A.;
      Pages: 1864 - 1875
      Abstract: In recent decades, building and tree detection from LiDAR data and aerial imagery with high automation and accuracy level has been the focus of many researchers which was selected as the purpose of our research. At first, after data preprocessing, off-terrain objects (OTO) including trees and buildings were extracted from LiDAR data. Second, a number of features were produced as inputs of support vector machines (SVMs) to separate buildings from trees. In the SVM, an automatic procedure was used for selecting the training data. After separating the trees, mathematical morphology operations were used to eliminate small objects and fill small holes in the detected buildings and trees. Finally, k-means clustering algorithm was used to separate buildings with different heights. The obtained results for detected buildings and trees were evaluated by working group III/4 of ISPRS, which demonstrated a high rate of success. For completeness, correctness, and quality metrics in per area mode, average values of 88.70%, 95.60%, and 85.30% for buildings and 74.30%, 63.50%, and 52.10% for trees were obtained, respectively.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • TanDEM-X for Large-Area Modeling of Urban Vegetation Height: Evidence from
           Berlin, Germany
    • Authors: Schreyer; J.;Geib, C.;Lakes, T.;
      Pages: 1876 - 1887
      Abstract: Large-area urban ecology studies often miss information on vertical parameters of vegetation, even though they represent important constituting properties of complex urban ecosystems. The new globally available digital elevation model (DEM) of the spaceborne TanDEM-X mission has an unprecedented spatial resolution (12 × 12 m) that allows us to derive such relevant information. So far, suitable approaches using a TanDEM-X DEM for the derivation of a normalized canopy model (nCM) are largely absent. Therefore, this paper aims to obtain digital terrain models (DTMs) for the subsequent computation of two nCMs for urban-like vegetation (e.g., street trees) and forest-like vegetation (e.g., parks), respectively, in Berlin, Germany, using a TanDEM-X DEM and a vegetation mask derived from UltraCam-X data. Initial comparisons between morphological DTM-filter confirm the superior performance of a novel disaggregated progressive morphological filter (DPMF). For improved assessment of a DTM for urban-like vegetation, a modified DPMF and image enhancement methods were applied. For forest-like vegetation, an interpolation and a weighted DPMF approach were compared. Finally, all DTMs were used for nCM calculation. The nCM for urban-like vegetation revealed a mean height of 4.17 m compared to 9.61 m of a validation nCM. For forest-like vegetation, the mean height for the nCM of the weighted filtering approach (9.16 m) produced the best results (validation nCM: 13.55 m). It is concluded that an nCM from TanDEM-X can capture vegetation heights in their appropriate dimension, which can be beneficial for automated height-related vegetation analysis such as comparisons of vegetation carbon storage between several cities.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Sparse Representation Denoising Framework for 3-D Building Reconstruction
           From Airborne LiDAR Data
    • Authors: Cao; Z.;Gu, Y.;
      Pages: 1888 - 1900
      Abstract: Random noise has a serious impact on the performance of three-dimensional (3-D) building reconstruction from airborne LiDAR data. In this paper, a sparse representation denoising framework for building roofs from airborne LiDAR is proposed. In the proposed framework, both the random noise and the local structural information are considered. At first, a systematic analysis for the random noise of the rasterized image of raw LiDAR data is presented in detail by taking the random noise of LiDAR data and its local structural information into consideration. With the proposed random noise model, the rasterized image can be adjusted into image with White Gaussian noise. Therefore, by adjusting the rasterized image with the proposed random noise model, sparse representation denoising framework designed for White Gaussian noise is employed in this paper. In order to realize the sparse representation-based denoising framework efficiently, multimanifolds structural dictionaries are learned from clean simulated data by employing K-SVD technique. Finally, three different implementations of the proposed denoising framework are proposed. Experimental results illustrate that the proposed denoising framework can efficiently restore the lost information caused by random noise of 3-D building roof data from airborne LiDAR with obvious improvement comparing with the classical K-SVD-based denoising method.83-D Building Reconstruction
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Building Types’ Classification Using Shape-Based Features and
           Linear Discriminant Functions
    • Authors: Wurm; M.;Schmitt, A.;Taubenbock, H.;
      Pages: 1901 - 1912
      Abstract: In this paper, the applicability and performance of linear discriminant analysis (LDA) for building types' classification are investigated. Building models at a level of detail 1 (LoD1) are derived from real estate cadastral building footprints and digital surface models from stereoscopic airborne images. In several experiments for two cities in Germany (Berlin and Munich), we first evaluate the discriminatory power of 26 different shape-based features which describe the physiognomy of individual buildings in terms of 1-D (e.g., length), 2-D (e.g., area), and 3-D (e.g., volume) features. While 1-D features show low contributions to the discrimination of the five building types, we observe high contributions of the 3-D shape index and 2-D measures of compactness. In a second group of experiments, the size of training samples for the classification process is investigated with the outcome that a size of 10% of the total number of labeled features is practicable in terms of size and accuracy. In a third battery of experiments, the selected features and training sample size are used for the classification of building types resulting in kappa values of 0.94 for both cities. In the final experiments, the geographical transfer between the two cities is investigated reaching kappa values of 0.93 and 0.91, respectively. The tests show that a simple linear classifier like LDA can handle building types' classification without much user interaction compared to more complex classification methods but is limited when similar building types (e.g., perimeter block development and block development) are to be discriminated.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Estimation of Seismic Vulnerability Levels of Urban Structures With
           Multisensor Remote Sensing
    • Authors: Geiss; C.;Jilge, M.;Lakes, T.;Taubenbock, H.;
      Pages: 1913 - 1936
      Abstract: The ongoing global transformation of human habitats from rural villages to ever growing urban agglomerations induces unprecedented seismic risks in earthquake prone regions. To mitigate affiliated perils requires the seismic assessment of built environments. Numerous studies emphasize that remote sensing can play a valuable role in supporting the extraction of relevant features for preevent vulnerability analysis. However, the majority of approaches operate on building level. This induces the deployment of very high spatial resolution remote sensing data, which hampers, nowadays, utilization capabilities for larger areas due to data costs and processing requirements. In this paper, we alter the spatial scale of analysis and propose concepts and methods to estimate the seismic vulnerability level of homogeneous urban structures. A procedure is designed, which comprises four main steps dedicated to: 1) delineation of urban structures by means of a tailored unsupervised data segmentation procedure with scale optimization; 2) characterization of urban structures by a joint exploitation of multisensor data; 3) selection of most feasible features under consideration of in situ vulnerability information; and 4) estimation of seismic vulnerability levels of urban structures within a supervised learning framework. We render the prediction problem in three ways to address operational requirements that can evolve in real-life situations. 1) To discriminate two or more classes based on labeled samples of all classes present in the data under investigation, we use the framework of soft margin support vector machines (C-SVM). 2) To consider situations, where solely labeled samples are available for the class(es) of interest and not for all classes present in the data, we deploy ensembles of ν-one-class SVM (ν-OC-SVM). and 3) To fit data with a higher statistical level of measurement (interval or ratio scale), we utilize a support vector regression (SVR) approach to- estimate a regression function from the training samples. Experimental results are obtained for the earthquake-prone mega city Istanbul, Turkey. We use multispectral data from the RapidEye constellation, elevation measurements from the TanDEM-X mission, and spatiotemporal analyses based on data from the Landsat archive to characterize the urban environment. In addition, different in situ data sets are incorporated for Istanbul's district Zeytinburnu and the residual settlement area of Istanbul. When estimating damage grades for Zeytinburnu with SVR, best models are characterized by mean absolute percentage errors less than 11%, and fairly strong goodness of fit (R > 0.75). When aiming to identify different types of urban structures for the remaining settlement area of Istanbul (i.e., urban structures determined by large industrial/commercial buildings and tall detached residential buildings, which can be considered here as highly and slightly vulnerable, respectively), results obtained with C-SVM show a distinctive increase of accuracy compared to results obtained with ensembles of ν-OC-SVM. The latter were not able to exceed moderate agreements, with κ statistics slightly above 0.45. Instead, C-SVM allowed obtaining ν statistics expressing substantial and even excellent agreements (κ > 0.6 up to κ > 0.8). Overall, analyzes provide very promising empirical evidence, which confirms the potential of remote sensing to support seismic vulnerability assessment.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Building Damage Detection Using Object-Based Image Analysis and ANFIS From
           High-Resolution Image (Case Study: BAM Earthquake, Iran)
    • Authors: Janalipour; M.;Mohammadzadeh, A.;
      Pages: 1937 - 1945
      Abstract: Building damage detection after earthquake would help to rapid relief and response of disaster. In this study, an efficient method was proposed for building damage detection in urban area after earthquake using pre-event vector map and postevent pan-sharpened high spatial resolution image. At first, preprocessing was applied on the postevent satellite image. Second, results of pixel- and object-based classifications were integrated. In the following, geometric features of buildings were extracted including area, rectangular fit (rect_fit), and convexity. A decision-making system based on these features and an adaptive network-based fuzzy inference system (ANFIS) model was designed to attain building damage degree. A comprehensive sensitivity analysis was carried out to find proper parameters of the ANFIS model leading to accurate damage results. The proposed method was tested over earthquake data set of Bam city in Iran. The results of our method indicate that an overall accuracy of 76.36% and kappa coefficient of 0.63 were achieved to identify building damage degree. The obtained results indicate that the postevent geometrical features (relative change of different damage levels with respect to each other) along with the ANFIS model can help to reach better results in building damage detection.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Road Extraction From Very High Resolution Remote Sensing Optical Images
           Based on Texture Analysis and Beamlet Transform
    • Authors: Ouled Sghaier; M.;Lepage, R.;
      Pages: 1946 - 1958
      Abstract: Road extraction from very high resolution sensors is a very popular topic in panchromatic and multispectral remote sensing image analysis. Despite the vast number of methods proposed in the literature to deal with this problem, in practice, most are quite limited and do not account for geometric and radiometric variability. Our aim is to propose a novel road extraction approach able to efficiently extract roads and reduce computation time using texture analysis and multiscale reasoning based on the beamlet transform. The proposed methodology consists of two stages: 1) road edge candidate selection and 2) multiscale reasoning with the beamlet transform. In the first step, mathematical morphology is applied to distinguish rectilinear structures, and road edge candidates are identified using the Canny edge detector. In the second phase, multiscale reasoning using the beamlet transform allows local and global information to be combined. Global information is introduced to distinguish main road axes at coarser scales, and local segments in finer scales, which are aggregated to reconstruct the road network. Rules based on the spatial relationships between segments belonging to different levels of resolution are also introduced at this stage. The experiments are performed based on the images acquired from the city of Port-au-Prince in Haiti during the earthquake of January 2010. The results demonstrate the accuracy and efficiency of our algorithm.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • A Comparison of Small-Area Population Estimation Techniques Using
           Built-Area and Height Data, Riyadh, Saudi Arabia
    • Authors: Alahmadi; M.;Atkinson, P.;Martin, D.;
      Pages: 1959 - 1969
      Abstract: Small-area population estimation is important for many applications. This paper explores the usefulness of Landsat ETM + data, remotely sensed height data, census population, and dwelling unit data to provide small-area population estimates. Riyadh, Saudi Arabia was selected as a suitable area to test a set of methods for population downscaling. Two broad approaches were applied: 1) statistical modeling and 2) areal interpolation. With regard to statistical modeling, regression through the origin was used to model the relationship between density of dwelling units and built area proportion at the block level and the coefficients were used to downscale the density of dwelling units to the parcel level. Areal interpolation with ancillary data (dasymetric mapping) used the block and parcel levels as the source and target zones, respectively. The population distribution was then estimated based on the average population per dwelling unit. Eight models were developed and tested. A conventional regression model, using only built area as a covariate, was used as a benchmark and compared with the more sophisticated models. Remotely sensed height data were used to: 1) create number of floors; 2) classify the built area into different categories; and 3) increase the user's accuracy of the built area. It was found that remotely sensed height data were useful to explain the variation in the dependent variable across the selected study area. Dasymetric mapping was applied in order to provide a comparison, while acknowledging that the method uses population data not available in the regression approach.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Determining the Relationship Between Census Data and Spatial Features
           Derived From High-Resolution Imagery in Accra, Ghana
    • Authors: Sandborn; A.;Engstrom, R.N.;
      Pages: 1970 - 1977
      Abstract: Remotely sensed-based estimates of dwelling and population characteristics can provide timely and spatially explicit information for urban planning and development in emerging cities. This exploratory analysis quantifies spatial features of built-up areas derived from high-resolution satellite imagery and directly relates them to census-derived variables in Accra, Ghana. Spatial features are image metrics that analyze pixel groups in order to describe the geometry, orientation, and patterns of objects in an image. By using spatial features in an urban setting, city infrastructure variations, such as roads and buildings, can be quantified and related to census variables, such as living standards and housing conditions. To test the associations between spatial patterns and demographic variables, five spatial features (line support regions, PanTex, histograms of oriented gradients, local binary patterns, and Fourier transform) were quantified and extracted from the imagery, and then correlated to the census variables. Findings indicate both spatial features and spectral information (such as NDVI) correlate strongly with standards of living such as population and housing density. Results from this study suggest that spatial features derived from satellite imagery can be used to help map socioeconomic characteristics within the city of Accra, Ghana, and that this methodology may be transferable to other developing cities.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • A New European Settlement Map From Optical Remotely Sensed Data
    • Authors: Florczyk; A.J.;Ferri, S.;Syrris, V.;Kemper, T.;Halkia, M.;Soille, P.;Pesaresi, M.;
      Pages: 1978 - 1992
      Abstract: An application of a general methodology for processing very high-resolution imagery to produce a European Settlement Map (ESM) in support of policy-makers is presented. The process mapped around 10 million km2 of the European continent. The input image data are satellite SPOT-5/6 pan-sharpened multispectral images of 2.5- and 1.5-m spatial resolution, respectively. This is the first time that remote sensing technology demonstrates capability to produce a continental information layer using 2.5-m input images. Moreover, it is the highest resolution continental map produced so far. The presented workflow is data-driven and consists in fully automatic image information extraction based on textural and morphological image analysis. The learning method allows the processing of high-resolution image data using coarse resolution thematic layers as reference. Validation shows an overall accuracy of 96% with omission and commission errors less than 4% and 1%, respectively.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Semi-supervised Hierarchical Clustering for Semantic SAR Image Annotation
    • Pages: 1993 - 2008
      Abstract: In this paper, we propose a semi-automated hierarchical clustering and classification framework for synthetic aperture radar (SAR) image annotation. Our implementation of the framework allows the classification and annotation of image data ranging from scenes up to large satellite data archives. Our framework comprises three stages: 1) each image is cut into patches and each patch is transformed into a texture feature vector; 2) similar feature vectors are grouped into clusters, where the number of clusters is determined by repeated cluster splitting to optimize their Gaussianity; and 3) the most appropriate class (i.e., a semantic label) is assigned to each image patch. This is accomplished by semi-supervised learning. For the testing and validation of our implemented framework, a concept for a two-level hierarchical semantic image content annotation was designed and applied to a manually annotated reference dataset consisting of various TerraSAR-X image patches with meter-scale resolution. Here, the upper level contains general classes, while the lower level provides more detailed subclasses for each parent class. For a quantitative and visual evaluation of the proposed framework, we compared the relationships among the clustering results, the semi-supervised classification results, and the two-level annotations. It turned out that our proposed method is able to obtain reliable results for the upper-level (i.e., general class) semantic classes; however, due to the too many detailed subclasses versus the few instances of each subclass, the proposed method generates inferior results for the lower level. The most important contributions of this paper are the integration of modified Gaussian-means and modified cluster-then-label algorithms, for the purpose of large-scale SAR image annotation, as well as the measurement of the clustering and classification performances of various distance metrics.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • A General Framework for Urban Area Extraction Exploiting Multiresolution
           SAR Data Fusion
    • Pages: 2009 - 2018
      Abstract: This work is devoted to the presentation of a general framework for multiresolution synthetic aperture radar (SAR) data fusion for the purpose of urban area extraction. Within this framework, prerequisites for a reasonable analysis of SAR data from different sensors and spatial resolutions as well as the state of the art of fusion techniques are discussed. Furthermore, several fusion approaches on the pixel, feature, and decision level are applied on multiscale SAR data sets in a uniform experimental setup over four test sites: 1) Sao Paulo (Brazil); 2) Beijing; 3) Guangzhou; and 4) Shanghai (People's Republic of China). The accuracy of the resulting urban maps is assessed quantitatively via comparison against manually generated reference data sets. Furthermore, advantages and drawbacks of the applied fusion methods are evaluated.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Downscaling of Landsat and MODIS Land Surface Temperature Over the
           Heterogeneous Urban Area of Milan
    • Authors: Bonafoni; S.;
      Pages: 2019 - 2027
      Abstract: Remotely sensed images of land surface temperature (LST) with high spatial resolution are required for various environmental applications. For instance, finer resolutions (FRs) are essential to capture thermal details in urban textures. To meet the requirements of sharper and sharper images, this study carries out a downscaling from coarser spatial resolution LST images to FRs using relationships between LST and spectral indexes (SIs) representative of different land cover types over the heterogeneous area of Milan. Different regressive schemes were applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS images during four summer passages. The regressions were first evaluated on Landsat images aggregated at 960 m resolution and downscaled to 480, 240, and 120 m. For the four Landsat scenes, the best regression models include both vegetation and built-up/soil SIs: the root mean square (rms) error, around 1 K for 480 m and 2 K for 120 m, is clearly below the LST standard deviation of each reference image, assumed as LST spatial variability. Then, contemporary MODIS data were downscaled from 960 m to the above FRs, and the best models include again both vegetation and built-up/soil SIs. The rms error is higher than the correspondent Landsat one (in some cases exceeds 3 K), but always below the LST spatial variability. A compression of the range of LST values for the MODIS-downscaled images was found with respect to the Landsat disaggregated images: this shortcoming in the LST retrieval affects the MODIS downscaling accuracy.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Quantifying Spatial–Temporal Pattern of Urban Heat Island in
           Beijing: An Improved Assessment Using Land Surface Temperature (LST) Time
           Series Observations From LANDSAT, MODIS, and Chinese New Satellite
           GaoFen-1
    • Authors: Liu; K.;Su, H.;Li, X.;Wang, W.;Yang, L.;Liang, H.;
      Pages: 2028 - 2042
      Abstract: The purpose of this study is to comprehensively quantify the spatial-temporal patterns of surface urban heat island (SUHI) by investigating the relationship between Land surface temperature (LST) and the land-cover types and associated landscape components in the case of Beijing, China. The spatial and temporal adaptive reflectance fusion model (STARFM) developed by Gao et al. was employed to create the high spatial resolution LST time series, using LST data from the MODIS/Terra and LANDSAT 8 over the period from May to November in 2013. This paper also investigated the application of the Chinese new high spatial resolution satellite GaoFen-1 in urban thermal environments studies which were insufficiently studied previously. The impacts of four landscape metrics (LSMs) on urban LST were investigated on the basis of two scenes of GaoFen images acquired in 2013 summer (June 19 and August 10). Results showed that SUHI effect was prevalent in Beijing from May to October. The intensity of SUHI magnitude was found accentuated mainly in the summer months (July and August), indicating that the trend of surface UHI effect is inconsistent with that of canopy-layer UHI effect. No obvious linear relationships were observed between subplot LST and impervious surfaces LSMs. However, four impervious surfaces LSMs were correlated well with the temporal dynamics of LST. We also found the configurational patterns of green space could put substantial influences as strong as corresponding compositional patterns and the lower vegetation coverage in downtown could better account for the urban LST.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Web-Enabled Landsat Data Time Series for Monitoring Urban Heat Island
           Impacts on Land Surface Phenology
    • Authors: Krehbiel; C.P.;Jackson, T.;Henebry, G.M.;
      Pages: 2043 - 2050
      Abstract: Urbanization increases the impacts of cities on the natural environment, in part by altering local environmental conditions. The Web-Enabled Landsat Data (WELD) archive (2003-2012) provides an opportunity to analyze the impacts of urbanization and urban heat islands (UHIs) on vegetation dynamics in and around cities. Here, we modeled the WELD normalized difference vegetation index (NDVI) product as a convex quadratic function of thermal time and derived land surface phenology (LSP) metrics to investigate the influence of UHIs on LSP along an urban-rural gradient and to characterize the response of vegetation to urbanization for two cities in the U.S. Northern Great Plains. Results show that for perennial vegetation, proximity to city center is positively associated with increased duration of growing season in thermal time. We found a linear relationship between the modeled rate of vegetation green-up and the peak height of NDVI for developed and forest pixels and after croplands were converted to developed land covers.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Inverse Air-Pollutant Emission and Prediction Using Extended Fractional
           Kalman Filtering
    • Pages: 2051 - 2063
      Abstract: It is essential to maintain air-quality standards and to take necessary measures when air-pollutant concentrations exceed permissible limits. Pollutants such as ground-level ozone (O3), nitrogen oxides (NOX), and volatile organic compounds (VOCs) emitted from various sources can be estimated at a particular location through integration of observation data obtained from measurement sites and effective air-quality models, using emission inventory data as input. However, there are always uncertainties associated with the emission inventory data as well as uncertainties generated by a meteorological model. This paper addresses the problem of improving the inverse air pollution emission and prediction over the urban and suburban areas using the air-pollution model with chemical transport model (TAPM-CTM) coupled with the extended fractional Kalman filter (EFKF) based on a Matérn covariance function. Here, nitrogen oxide (NO), nitrogen dioxide (NO2), and O3 concentrations are predicted by TAPM-CTM in the airshed of Sydney and surrounding areas. For improvement of the emission inventory, and hence the airquality prediction, the fractional order of the EFKF is tuned using a genetic algorithm (GA). The proposed methodology is verified with measurements at monitoring stations and is then applied to obtain a better spatial distribution of O3 over the region.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Determining the Type and Starting Time of Land Cover and Land Use Change
           in Southern Ghana Based on Discrete Analysis of Dense Landsat Image Time
           Series
    • Authors: Shih; H.-S.;Stow, D.;Weeks, J.R.;Coulter, L.L.;
      Pages: 2064 - 2073
      Abstract: Rural to urban migration and relatively high fertility rates have influenced rapid land cover and land use change (LCLUC) in southern Ghana, which warrants more frequent monitoring. We develop and test approaches for semiautomatically and more frequently identifying the type and date of LCLUC from time series of Landsat ETM+ imagery from 2000 to 2014. Clouds, cloud shadows, and scan line corrector-off create missing data in ETM+ images. Forty-one dates of ETM+ images that partially contain missing data were utilized. The general approach is to conduct a per-pixel supervised classification on each image of a Landsat time series after masking missing data. Spatial, temporal, and logical filters are applied to correct for misclassification and missing data. Each image is classified into three general classes: 1) Built; 2) Natural Vegetation; 3) and Agriculture, with expansion of Built being our main focus. Reference data for Change-to-Built were independently selected from all available high-spatial resolution satellite images (e.g., Quickbird, GeoEye, Worldview, and Google Earth imagery), and the type and beginning time of LCLUC was recorded. Results show that the temporal-filtered product identified both the location and the start of Change-to-Built more precisely and accurately than the nonfiltered and other filtered products. Based on reference data, 40% of the Change-to-Built samples were correctly identified without filtering; whereas, when a temporal filter was applied, 80% were correctly identified with low amounts of false positive Change-to-Built pixels. The temporal-filtered product has the highest temporal precision and accuracy (mean time difference = 2.1 years) in identifying the start of Change-to-Built.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Validation of Aqua-MODIS C051 and C006 Operational Aerosol Products Using
           AERONET Measurements Over Pakistan
    • Authors: Bilal; M.;Nichol, J.E.;Nazeer, M.;
      Pages: 2074 - 2080
      Abstract: The aim of this study was to evaluate the performance of the Aqua-MODIS (MYD04) collections 5.1 (C051) and 6 (C006) operational aerosol products over Pakistan. These include the Dark Target (DT) and the Deep Blue (DB) C051/C006 aerosol optical depth (AOD) observations at 10-km spatial resolution, and which were validated using 7 years (2007-2013) AOD measurements from two AERONET stations located in Pakistan's two largest cities, Lahore and Karachi. Lahore is an inland city, consisting of built-up areas and agricultural land and dominated by mainly fine aerosol particles. Karachi is an urban-coastal city with built-up areas and bare land, and mainly affected by coarse aerosol particles. The retrieval uncertainties and accuracy were evaluated using the expected error over land (EE : ±0.05 + 15%), the root-mean-square error (RMSE), the mean absolute error (MAE), and the relative mean bias (RMB). It was found that the DT C051 AOD retrievals were significantly overestimated over both AERONET sites with mean overestimation of 21% and 31% for Lahore and Karachi, respectively. On the other hand, the DB C051 retrievals were underestimated by 10% and 35% for both cities. Similar to the DT C051, the C006 AOD retrievals were overestimated over Lahore, but significantly improved over Karachi, as the mean overestimation reduced from 31% (RMB = 1.31) to 4% (RMB = 1.04) and the percentage of retrievals within the EE increased from 38.46% to 63.33%. However, the DB C006 AOD retrievals have similar errors (RMSE and MAE) and the percentage of retrievals within the EE over both cities as C051, but they have more mean underestimations. Spatio-temporal distributions showed that the DT and DB C006 were well correlated with Karachi and Lahore AERONET measurements, respectively. Therefore, these results recommend the use of the DT C006 algorithm over Karachi and the DB C006 over Lahore for qualitative regional air quality applications due to differences in land cover characteristi- s and aerosol types.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Combinational Build-Up Index (CBI) for Effective Impervious Surface
           Mapping in Urban Areas
    • Authors: Sun; G.;Chen, X.;Jia, X.;Yao, Y.;Wang, Z.;
      Pages: 2081 - 2092
      Abstract: The distribution of urban impervious surface is a significant indicator of the degree of urbanization, as well as a major indicator of environmental quality. Hence, taking advantage of remotely sensed imagery to map impervious surface has become an important topic. Spectral indices have been developed due to its convenience to apply, among which feature extraction approach has shown superiority in reliability and applicability. However, impervious surface is often confused with bare soil when the current existing indices are used as well as their sensor-specific limitations. In this study, a new index, combinational build-up index (CBI), is proposed to extract impervious surface. The new index combines the first component of a principal component analysis (PC1), normalized difference water index (NDWI), and soil-adjusted vegetation index (SAVI), representing high albedo, low albedo, and vegetation, respectively, to reduce the original bands into three thematic-oriented features. The new index was tested using various remote sensing images at different spectral and spatial resolutions. Qualitative and quantitative assessments of the accuracy and separability of CBI, together with the comparison with other existing indices, were performed. The result of this study indicates that the proposed method is able to serve as an effective impervious index and can be applied widely.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Call for papers: Special issue on new challenges and opportunities in
           scatterometry
    • Pages: 2093 - 2093
      Abstract: Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Call for papers: Special issue on microwave radiometry and remote sensing
           of the environment
    • Pages: 2094 - 2094
      Abstract: Describes the above-named upcoming special issue or section. May include topics to be covered or calls for papers.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Expand Your Network, Get Rewarded
    • Pages: 2095 - 2095
      Abstract: Advertisement: IEEE.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
  • Introducing IEEE Collabratec
    • Pages: 2096 - 2096
      Abstract: IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technical interests, or career pursuits. You can also create and share a professional identity that showcases key accomplishments and participate in groups focused around mutual interests, actively learning from and contributing to knowledgeable communities. All in one place! Learn about IEEE Collabratec at ieeecollabratec.org.
      PubDate: May 2016
      Issue No: Vol. 9, No. 5 (2016)
       
 
 
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