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Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of

Issue 5 • Date Oct. 2013

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  • Front cover

    Page(s): C1
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  • IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing publication information

    Page(s): C2
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  • Table of contents

    Page(s): 2073 - 2074
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  • Foreword to the special issue on earth observation approaches for large area land monitoring with multiple sensors and resolutions

    Page(s): 2075 - 2076
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  • Moving Car Detection and Spectral Restoration in a Single Satellite WorldView-2 Imagery

    Page(s): 2077 - 2087
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    A novel approach to automatic detection of moving vehicles from a single satellite WorldView-2 imagery is presented. The technique is based on the time gap between three sensor band groups: panchromatic, multispectral, and four new additional multispectral bands. The entire process is automatic and includes movement estimation followed by moving object spectral restoration and construction of spatially built object profiles to estimate the movement direction and velocity. The approach neither relies on external information like road data or site models, nor is limited to vehicle type. The performance of the new approach is demonstrated via detection of several vehicle types on WorldView-2 satellite imagery of the San Francisco area. View full abstract»

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  • A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping

    Page(s): 2088 - 2101
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    Information on the changing land surface is required at high spatial resolutions as many processes cannot be resolved using coarse resolution data. Deriving such information over large areas for Landsat data, however, still faces numerous challenges. Image compositing offers great potential to circumvent such shortcomings. We here present a compositing algorithm that facilitates creating cloud free, seasonally and radiometrically consistent datasets from the Landsat archive. A parametric weighting scheme allows for flexibly utilizing different pixel characteristics for optimized compositing. We describe in detail the development of three parameter decision functions: acquisition year, day of year and distance to clouds. Our test site covers 42 Landsat footprints in Eastern Europe and we produced three annual composites. We evaluated seasonal and annual consistency and compared our composites to BRDF normalized MODIS reflectance products. Finally, we also evaluated how well the composites work for land cover mapping. Results prove that our algorithm allows for creating seasonally consistent large area composites. Radiometric correspondence to MODIS was high (up to R2 > 0.8), but varied with land cover configuration and selected image acquisition dates. Land cover mapping yielded promising results (overall accuracy 72%). Class delineations were regionally consistent with minimal effort for training data. Class specific accuracies increased considerably (~10%) when spectral metrics were incorporated. Our study highlights the value of compositing in general and for Landsat data in particular, allowing for regional to global LULCC mapping at high spatial resolutions. View full abstract»

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  • A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results

    Page(s): 2102 - 2131
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    A general framework for processing high and very-high resolution imagery in support of a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 million km2 of the Earth surface spread in four continents, corresponding to an estimated population of 1.3 billion people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS 2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye 1, QuickBird 2, Ikonos 2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, band, resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted. View full abstract»

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  • Data Mining, A Promising Tool for Large-Area Cropland Mapping

    Page(s): 2132 - 2138
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (668 KB) |  | HTML iconHTML  

    The northern fringe of sub-Saharan Africa is a region that is considered to be particularly vulnerable to climate variability and change, and it is a location in which food security remains a major challenge. To address these issues, it is essential to develop global data sets of the geographic distribution of agricultural land use. The objectives of this study were to test an original data mining approach for classifying and mapping the cropped land in West Africa using coarse-resolution imagery and to compare the classification results with those obtained from a classic ISODATA approach. The data mining approach is able to handle large volumes of data and is based on different descriptors (65) of the land use, including the spatial and temporal satellite-derived metrics of 12 MODIS NDVI 16-day composite images and the static attributes taken from field surveys. The classic ISODATA method showed that 68.3% of pixels from a SPOT reference map were correctly classified in three validation sites versus 57.8% for the data mining approach. Validation by field observations showed equivalent results for both methods with an F-score of 0.72. The results of this study demonstrated the relevance of the use of data-mining tools for large-area monitoring. View full abstract»

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  • Trend Analysis of Global MODIS-Terra Vegetation Indices and Land Surface Temperature Between 2000 and 2011

    Page(s): 2139 - 2145
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    Previous works have shown that the combination of vegetation indices with land surface temperature (LST) improves the analysis of vegetation changes. Here, global MODIS-Terra monthly data from 2000 to 2011 were downloaded and organized into LST, NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) time series. These time series were then corrected from cloud and atmospheric residual contamination through the IDR (iterative Interpolation for Data Reconstruction) method. Then, statistics were retrieved from both corrected time series, and the YLCD (Yearly Land Cover Dynamics) approach has been applied to data sources (NDVI-LST and EVI-LST) to analyze changes in the vegetation. Finally, trends were retrieved and their statistical significance was assessed through the Mann-Kendall statistical framework. Global statistics show that both data sets lead to similar trends, as is the case for the spatial distribution of observed trends. These trends confirm previous results as well as prediction of climate warming consequences, such as a marked increase in boreal temperatures. View full abstract»

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  • Large-Area Remote Sensing in High-Altitude High-Speed Platform Using MIMO SAR

    Page(s): 2146 - 2158
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    Large-area mapping is of great valuable in microwave remote sensing, but it is a contradiction between swath width and azimuth resolution due to the minimum antenna area constraint. In this paper, we consider a specific wide-area mapping technique with high-altitude high-speed platform, where range ambiguity suppression is a technical challenge. To resolve this problem, we present a multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) with multiple antennas placed in the cross-track direction. The increased degrees-of-freedom (DOFs) provide a potential to resolve possible range and/or azimuth ambiguities. After formalizing the system scheme and signal model, an iterative matched filtering algorithm is presented, which can efficiently suppress the cross-correlation interferences in the multichannel data separation. Furthermore, a range-Doppler based image formation algorithm is derived. The MIMO SAR system performance is evaluated by the range-ambiguity-to-signal ratio (RASR) performance. Numerical simulation results validate the effectiveness of the proposed MIMO SAR in high-altitude high-speed platform SAR for large-area remote sensing. View full abstract»

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  • Feature Level Fusion of Multi-Temporal ALOS PALSAR and Landsat Data for Mapping and Monitoring of Tropical Deforestation and Forest Degradation

    Page(s): 2159 - 2173
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    Many tropical countries suffer from persistent cloud cover inhibiting spatially consistent reporting of deforestation and forest degradation for REDD+. Data gaps remain even when compositing Landsat-like optical satellite imagery over one or two years. Instead, medium resolution SAR is capable of providing reliable deforestation information but shows limited capacity to identify forest degradation. This paper describes an innovative approach for feature fusion of multi-temporal and medium-resolution SAR and optical subpixel fraction information. After independently processing SAR and optical input data streams the extracted SAR and optical subpixel fraction features are fused using a decision tree classifier. ALOS PALSAR Fine Bean Dual and Landsat imagery of 2007 and 2010 acquired over the main mining district in central Guyana have been used for a proof-of-concept demonstration observing overall accuracies of 88% and 89.3% for mapping forest land cover and detecting deforestation and forest degradation, respectively. Deforestation and degradation rates of 0.1% and 0.08% are reported for the observation period. Data gaps due to mainly clouds and Landsat ETM+ SLC-off that remained after compositing a set of single-period Landsat scenes, but also due to SAR layover and shadow could be reduced from 7.9% to negligible 0.01% while maintaining the desired thematic detail of detecting deforestation and degradation. The paper demonstrates the increase of both spatial completeness and thematic detail when applying the methodology, compared with potential Landsat-only or PALSAR-only approaches for a heavy cloud contaminated tropical environment. It indicates the potential for providing the required accuracy of activity data for REDD+ MRV. View full abstract»

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  • Method for Large-Area Satellite Image Quality Enhancement With Local Aerial Images Based on Non-Target Multi-Point Calibration

    Page(s): 2174 - 2183
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    This paper designed a non-target multi-point calibration method for the quality enhancement of large-area satellite images by using local aerial images. Satellite images are more sensitive to atmospheric effects compared with aerial images. Atmospheric effects on aerial images are even negligible in fine weather. Given that aerial remote sensing has high spatial resolution and geometric fidelity, more spatial details can be recorded in aerial images. However, the scan bandwidth of aerial images is limited compared with that of satellite images. Thus, taking high-quality aerial images of a neighborhood as reference can provide prior knowledge for point spread function (PSF) estimation and for the quality enhancement of large-area satellite images. The least square method and interpolation are used for the PSF estimation of spatial variation, and then total variation minimization is used for recovery. The results show that the designed method can effectively enhance the quality of large-area satellite images. View full abstract»

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  • Fast and Efficient Urban Extent Extraction Using ASAR Wide Swath Mode Data

    Page(s): 2184 - 2195
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1899 KB) |  | HTML iconHTML  

    This paper aims at introducing a fast and efficient approach able to extract human settlement extents using ASAR Wide Swath Mode data. The proposed approach exploits the spatial features characterizing human settlements in SAR data at a spatial resolution around 100 m, i.e., long term coherence and large backscattered power values. The joint use of multi-temporal filtering and averaging and the homogeneously high SAR return from built-up structures is the key to extract quickly and robustly human settlement extents. Although prone to commission errors in mountainous areas, the procedure proposed in this paper proved to be able to extract consistently more accurate results than existing global data sets including Globcover 2009. This was assessed by running a series of tests in different geographical areas and comparing the new and the existing products with independently extracted “urban” and “non-urban” points. The results show that ASAR data have no fewer potential than optical ones for global mapping of human settlements. Properly processed, instead, SAR data are able to provide an effective solution to the need of a global map of human settlement, useful for risk computations, climate change model inputs and population mapping, among other applications. View full abstract»

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  • Robust Extraction of Urban Land Cover Information From HSR Multi-Spectral and LiDAR Data

    Page(s): 2196 - 2211
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    This paper focuses on the description and demonstration of a simple, but effective object-based image analysis (OBIA) approach to extract urban land cover information from high spatial resolution (HSR) multi-spectral and light detection and ranging (LiDAR) data. Particular emphasis is put on the evaluation of the proposed method with regard to its generalization capabilities across varying situations. For this purpose, the experimental setup of this work includes three urban study areas featuring different physical structures, four sets of HSR optical and LiDAR input data, as well as statistical measures to enable the assessment of classification accuracies and methodological transferability. The results of this study highlight the great potential of the developed approach for accurate, robust and large-area mapping of urban environments. User's and producer's accuracies observed for all maps are almost consistently above 80%, in many cases even above 90%. Only few larger class-specific errors occur mainly due to the simple assumptions on which the method is based. The presented feature extraction workflow can therefore be used as a template or starting point in the framework of future urban land cover mapping efforts. View full abstract»

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  • An Area-Based Image Fusion Scheme for the Integration of SAR and Optical Satellite Imagery

    Page(s): 2212 - 2220
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    The task of enhancing the perception of a scene by combining information captured from different image sensors is usually known as multisensor image fusion. This paper presents an area-based image fusion algorithm to merge SAR (Synthetic Aperture Radar) and optical images. The co-registration of the two images is first conducted using the proposed registration method prior to image fusion. Segmentation into active and inactive areas is then performed on the SAR texture image for selective injection of the SAR image into the panchromatic (PAN) image. An integrated image based on these two images is generated by the novel area-based fusion scheme, which imposes different fusion rules for each segmented area. Finally, this image is fused into a multispectral (MS) image through the hybrid pansharpening method proposed in previous research. Experimental results demonstrate that the proposed method shows better performance than other fusion algorithms and has the potential to be applied to the multisensor fusion of SAR and optical images. View full abstract»

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  • Stereoscopic Road Network Extraction by Decision-Level Fusion of Optical and SAR Imagery

    Page(s): 2221 - 2228
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    A new stereoscopic road network extraction framework based on the decision-level fusion of optical and Synthetic Aperture Radar (SAR) imagery is proposed in this paper. Three steps are included in this framework: 1) road segment extraction and structure optimization through SAR imagery, 2) road segment extraction and stereoscopic information collection through optical imagery, and 3) fusion of the SAR result with the optical image and the stereoscopic information. In this study, our new road network grouping algorithm called road network grouping based on the multi-scale geometric analysis of detector Response is used, with the improved footprint method, and the stereoscopic inversion algorithm. The most important finding of our work lies in the fusion step, by which a stereoscopic road network can be acquired after going through the three aforementioned processes and by fusing the stereoscopic information obtained from optical imagery and road network extracted from SAR imagery. Our algorithm is tested on the real TerraSAR-X and QuickBird data. View full abstract»

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  • Automatic Co-Registration of Optical Satellite Images and Airborne Lidar Data Using Relative and Absolute Orientations

    Page(s): 2229 - 2237
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    Co-registration of 2-D images and 3-D lidar points in a common area is an important task in data fusion as well as other applications. As the information acquired by image and lidar systems are not the same, the registration of heterogeneous sensors is a challenging issue. The objective of this study is to perform the co-registration of 2-D images and 3-D lidar points using relative and absolute orientations. The proposed method performs image matching between stereo images for relative orientation modeling and generates a matched 3-D surface model. Then, an automatic least squares 3-D surface matching is applied between matched 3-D surface model and lidar 3-D points. Finally, the precise object-to-image transformation and orthoimages can be generated via relative and absolute transformation. The test data include WorldView-2 image, QuickBird image and lidar data. The experimental results indicate that the relative orientation may reach subpixel accuracy while the absolute orientation may reach 1 pixel accuracy. Moreover, the geometric consistency between orthoimages is better than 0.5 m on the ground. View full abstract»

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  • A Land Cover Variation Model of Water Level for the Floodplain of Tonle Sap, Cambodia, Derived From ALOS PALSAR and MODIS Data

    Page(s): 2238 - 2253
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    The floodplain around Tonle Sap, Cambodia is strongly influenced by seasonal variations in water level. In the wet season, lacustrine landforms and vegetated areas are partly inundated due to increases in the water level. Conversely, they are gradually emerged when the flooding recedes during the dry season. Because floods in Tonle Sap are an annual event, a land cover variation model that takes into account water level is necessary to predict areal changes in each land cover class at the floodplain. To establish this model, we used the Phased Array L-band Synthetic Aperture Radar (PALSAR) backscattering coefficients, normalized difference vegetation index (NDVI) values, and tasseled cap (TC) transformations of Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2007 to 2010 to estimate the areal variation of six land cover classes during the annual flood pulse. The radar backscattering coefficients correlated well with NDVI values during the dry season, but the relationship vanished during the wet season. According to our model, a backscattering coefficient change from -8.4 dB to -20.6 dB for lowland shrubs in the flood developing stage corresponded to an areal percentage change of un-flooded lowland shrubs from 16.3% to 0.5% of the total study area. Once the water level increased to the peak of flooding, 46.2% of the lowland shrub area was immersed. Our model also predicted that approximately 41.8% of the total study area was replaced with a water surface at the peak of flooding. When we compared the two results obtained using our model at 6 m above mean sea level (amsl) and using a digital terrain model (DTM) and the land use map, we observed a large difference between the two models in the areal percentage of the corresponding land cover of un-flooded lowland shrubs (-10.3%). Our land cover variation model can be used to predict areal changes in land cover classes during flood development and recession stages, and can also provide ins- ght into flood dynamics, thereby enabling flood management in this region. View full abstract»

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  • Variations of Foliage Chlorophyll fAPAR and Foliage Non-Chlorophyll fAPAR (fAPAR _{\rm chl} , fAPAR _{\rm nonmathchar ) at the Harvard Forest

    Page(s): 2254 - 2264
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    In the last three decades, substantial advancements have been made in understanding the global carbon cycle. Some of these advancements involve using the fraction of absorbed photosynthetically active radiation (fAPAR) by an entire canopy (fAPARcanopy) and/or the Normalized Difference Vegetation Index (NDVI) in modeling studies. In spite of these advancements, large uncertainties still remain. Zhang (Remote Sens. Environ., 2005) [1] tried to mitigate some of these uncertainties with the concept of using fAPAR that is restricted to the foliage chlorophyll (fAPARchl) instead of the entire canopy. In this current study, we calculated fAPARcanopy, fAPARchl, and foliage non-chlorophyll fAPAR (fAPARnon-hl) for the Harvard Forest using a radiative transfer model and multi-temporal Earth Observing One (EO-1) Hyperion satellite images. The canopy-level proportions of foliar chlorophyll and non-chlorophyll absorption were determined at different seasons (spring, summer, autumn) in an effort to demonstrate temporal variations of three plant functional types: deciduous forest, coniferous forest, and grass. Comparisons were made for NDVI versus fAPARcanopy and for the Enhanced Vegetation Index (EVI) versus fAPARchl. In addition, EO-1 Hyperion images were utilized to simulate these new fAPARcanopy, fAPARchl, and fAPARnon-chl products at 60 m as prototypes for the proposed NASA HyspIRI satellite spectrometer. These products should prove useful for future terrestrial carbon cycle and ecosystem studies. View full abstract»

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  • First Results of Monitoring Nature Conservation Sites in Alpine Region by Using Very High Resolution (VHR) X-Band SAR Data

    Page(s): 2265 - 2274
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    The exploitation of X-band imagery is currently being used in the field of agriculture for the discrimination of crop types and is also recently addressed for challenging tasks such as surface roughness and soil moisture retrieval. We investigated the potential of multi-temporal COSMO-SkyMed data for the monitoring of Natura 2000 habitats in alpine region. Short revisit time of currently available VHR synthetic aperture radar instruments (like TerraSAR-X, COSMO-SkyMed etc.) and their all-weather (day/night) image acquisition capability provides an additional advantage of continuous monitoring of nature conservation sites in particular which are small in size. Preliminary analysis of VV and VH signals indicates a predominant effect of attenuation with respect to the volume contribution as expected in case of X-band for the reduced penetration capabilities in the canopy. Significant changes in the VV signals are mainly ascribed to grazing/mowing event which reduces the canopy layer and then the signal attenuation. The final results are in agreement with the ancillary data (Normalized Vegetation Index (NDVI)) and management rule record for Natura 2000 habitats present in the alpine region. View full abstract»

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  • A Review of Some Important Technical Problems in Respect of Satellite Remote Sensing of Chlorophyll-a Concentration in Coastal Waters

    Page(s): 2275 - 2289
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    With the development of quantitative ocean color remote sensing, estimation of chlorophyll-a concentration in the coastal waters has aroused increasing attention from researchers. Currently, researches are confronted with difficulty in improving the accuracy of chlorophyll-a concentration estimation for turbid waters. Atmospheric correction, chlorophyll-a concentration modeling, and scale effect have already been identified as three critical factors affecting coastal water remote sensing. The in-depth exploration of them will accelerate the research progress of ocean color remote sensing. The ultimate objective of atmospheric correction and scale effect correction is to accurately estimate active constituents of turbid coastal waters in an optical way. Accordingly, the chlorophyll-a concentration modeling is a basic problem to be resolved, while atmospheric correction is the essential one. The scale effect problem arises during the modeling procedure where unrealistic homogeneous assumption is taken to measure chlorophyll-a concentration from the realistic non-homogeneous pixel. In the coastal remote sensing field, these three problems have become the most important topics in the current researches, and they will remain be the hot topics in the future. View full abstract»

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  • Automatic Fuzzy Clustering Based on Adaptive Multi-Objective Differential Evolution for Remote Sensing Imagery

    Page(s): 2290 - 2301
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    Traditional automatic fuzzy clustering methods can obtain the optimal number of clusters by maximizing or minimizing one single-objective function using validity indexes. However, the effectiveness of these methods depends on the selection of the validity indexes, and one single-objective function may not provide satisfactory results because of the complexity of remote sensing images. For instance, the same land types may have different spectral curves, and different land types can have similar curves. To avoid this problem, this paper proposes a novel automatic fuzzy clustering method based on adaptive multi-objective differential evolution (AFCMDE) for remote sensing imagery. In AFCMDE, the automatic clustering problem is transformed into a multi-objective problem using two objective functions: Jm and the Xie-Beni index. AFCMDE is designed as a two-layer system comprising an optimization layer and a classification layer. In the optimization layer, AFCMDE searches for a feasible number of clusters by minimizing the Jm value and the Xie-Beni index. Based on the obtained number of clusters, AFCMDE utilizes non-dominated and crowd-distance sorting to obtain the optimal clustering centers and output the clustering results. In addition, a self-adaptive strategy without user-defined parameters is also used to improve the differential evolution. Experimental results using three different types of remote sensing image show that the AFCMDE algorithm consistently outperforms the other traditional clustering algorithms and the previous single-objective automatic fuzzy clustering algorithms. View full abstract»

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  • Satellite-Observed Nighttime Light Variation as Evidence for Global Armed Conflicts

    Page(s): 2302 - 2315
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    The objective of this research is to investigate the potential of nighttime light images, acquired with Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS), in evaluating global armed conflicts. To achieve this purpose, we assessed the relationship between armed conflicts and the satellite-observed nighttime light variation over 159 countries through annual composites of the nighttime light images. Firstly, a light ratio index was developed to reduce the data inconsistency of annual nighttime light images during 1992-2010. Then 12 countries were selected as examples for a primary investigation, and we found the outbreak of a war can reduce the light and the ceasefire can increase the light from the remote sensing images, which indicates armed conflict events always have significant impact on the nighttime light. Based on this assertion, a nighttime light variation index (NLVI) was developed to quantify the variation of the time series nighttime light. Then using conditional probability analysis, the probability of a country suffering from armed conflicts increases with increase of NLVI. Particularly, when the NLVI value is in a very high level as defined, 80% of the countries have experienced armed conflicts. Furthermore, using correlation analysis, the number of global armed conflicts is highly correlated with the global NLVI in temporal dimension, with a correlation coefficient larger than 0.77. In summary, the potential of nighttime light images in armed conflict evaluation is extended from a regional scale to a global scale by this study. View full abstract»

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  • A Rigorous SAR Epipolar Geometry Modeling and Application to 3D Target Reconstruction

    Page(s): 2316 - 2323
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    A rigorous epipolar geometry modeling for synthetic aperture radar (SAR) is developed from a concise imaging model proposed in the paper. The imaging model and epipolar model not only geometrically unify the SAR imaging and the optical camera imaging, but also motivate a 3D target reconstruction which is theoretically validated to be consistent with the radargrammetry and experimentally demonstrated to be accurate. View full abstract»

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  • Together, we are advancing technology

    Page(s): 2324
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Aims & Scope

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (J-STARS) addresses current issues and techniques in applied remote and in situ sensing, their integration, and applied modeling and information creation for understanding the Earth.

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Editor-in-Chief
Dr. Jocelyn Chanussot
Grenoble Institute of Technology