A comparison study of impervious surfaces estimation using optical and SAR remote sensing images

2012 ◽  
Vol 18 ◽  
pp. 148-156 ◽  
Author(s):  
Hongsheng Zhang ◽  
Yuanzhi Zhang ◽  
Hui Lin
Keyword(s):  
Remote Sensing ◽  
Impervious Surfaces ◽  
Comparison Study ◽  
Remote Sensing Images ◽  
Sar Remote Sensing

CONTEXTUAL CLASSIFICATION OF MULTISENSOR OPTICAL-SAR REMOTE SENSING IMAGES

2009 ◽  
pp. 307-328
Author(s):  
Gabriele Moser ◽  
Raffaele Gaetano ◽  
Gianni Poggi ◽  
Giuseppe Scarpa ◽  
Sebastiano B. Serpico
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Sar Remote Sensing

Harbor Detection Method of SAR Remote Sensing Images Based on Feature

2011 ◽  
Vol 32 (12) ◽  
pp. 2873-2878
Author(s):  
Qi Chen ◽  
Jun Lu ◽  
Ling-jun Zhao ◽  
Gang-yao Kuang
Keyword(s):  
Remote Sensing ◽  
Detection Method ◽  
Remote Sensing Images ◽  
Sar Remote Sensing

scholarly journals COMBINING SPECTRAL AND TEXTURE FEATURES USING RANDOM FOREST ALGORITHM: EXTRACTING IMPERVIOUS SURFACE AREA IN WUHAN

2016 ◽  
Vol XLI-B7 ◽  
pp. 351-358
Author(s):  
Zhenfeng Shao ◽  
Yuan Zhang ◽  
Lei Zhang ◽  
Yang Song ◽  
Minjun Peng
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Surface Area ◽  
Random Forests ◽  
Impervious Surface ◽  
Impervious Surfaces ◽  
Remote Sensing Images ◽  
Impervious Surface Area

Impervious surface area (ISA) is one of the most important indicators of urban environments. At present, based on multi-resolution remote sensing images, numerous approaches have been proposed to extract impervious surface, using statistical estimation, sub-pixel classification and spectral mixture analysis method of sub-pixel analysis. Through these methods, impervious surfaces can be effectively applied to regional-scale planning and management. However, for the large scale region, high resolution remote sensing images can provide more details, and therefore they will be more conducive to analysis environmental monitoring and urban management. Since the purpose of this study is to map impervious surfaces more effectively, three classification algorithms (random forests, decision trees, and artificial neural networks) were tested for their ability to map impervious surface. Random forests outperformed the decision trees, and artificial neural networks in precision. Combining the spectral indices and texture, random forests is applied to impervious surface extraction with a producer’s accuracy of 0.98, a user’s accuracy of 0.97, and an overall accuracy of 0.98 and a kappa coefficient of 0.97.

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