Abstract
Abstract. The study chooses the standard stripe and dual polarization SAR images of GF-3 as the basic data. Residential areas extraction processes and methods based upon GF-3 images texture segmentation are compared and analyzed. GF-3 images processes include radiometric calibration, complex data conversion, multi-look processing, images filtering, and then conducting suitability analysis for different images filtering methods, the filtering result show that the filtering method of Kuan is efficient for extracting residential areas, then, we calculated and analyzed the texture feature vectors using the GLCM (the Gary Level Co-occurrence Matrix), texture feature vectors include the moving window size, step size and angle, the result show that：window size is 11*11, step is 1, and angle is 0°, which is effective and optimal for the residential areas extracting. And with the FNEA (Fractal Net Evolution Approach), we segmented the GLCM texture images, and extracted the residential areas by threshold setting. The result of residential areas extraction verified and assessed by confusion matrix. Overall accuracy is 0.897, kappa is 0.881, and then we extracted the residential areas by SVM classification based on GF-3 images, the overall accuracy is less 0.09 than the accuracy of extraction method based on GF-3 Texture Image Segmentation. We reached the conclusion that，residential areas extraction based on GF-3 SAR texture image multi-scale segmentation is simple and highly accurate. although, it is difficult to obtain multi-spectrum remote sensing image in southern China, in cloudy and rainy weather throughout the year, this paper has certain reference significance.
Highlights
GF-3 satellite, the first C band and multi-polarization SAR satellite in China, is the Synthetic Aperture Radar (SAR) satellite mission with scientific and commercial applications, which was launched in August 2016
There are several methods for extracting residential area information from other highresolution SAR images, The main methods are the classification and extraction based on support vector machines(SVM), the method based on supervised classification of SAR texture images, and extracting residential area based on the combination of the digital number value and texture
We used GF-3 SAR images to generate gray level co-occurrence matrix texture images, performed multiscale segmentation on texture images, and extracted residential areas based on segmentation results. we contrasted the Energy, Entropy, Contrast, Correlation, variance, Mean, and Homogeneity of the gray level co-occurrence matrix, we use homogeneous texture features for multi-scale segmentation to extract residents area, homogeneity measures the value of the local variation of the SAR image texture, and a large value indicates that there is a lack of change between different regions of the image texture, and the locality is very uniform
Summary
GF-3 satellite, the first C band and multi-polarization SAR satellite in China, is the Synthetic Aperture Radar (SAR) satellite mission with scientific and commercial applications, which was launched in August 2016. Extraction of residential information for GF-3 images is relatively rare. There are several methods for extracting residential area information from other highresolution SAR images, The main methods are the classification and extraction based on support vector machines(SVM), the method based on supervised classification of SAR texture images, and extracting residential area based on the combination of the digital number value and texture. This paper presents a method of high-efficiency and highprecision extraction of residential areas process. We calculated the Gray Level Co-occurrence Matrix (GLCM) of GF-3 satellite images, and segmented the GLCM texture images through multi-scale segmentation of fractal network evolutionary algorithm, and we extracted the residential areas based on the image objects
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