Research on an Urban Building Area Extraction Method with High-Resolution PolSAR Imaging Based on Adaptive Neighborhood Selection Neighborhoods for Preserving Embedding

Bo Cheng,Chenbin Liang,Xiaoxiao Ma,Shiai Cui

doi:10.3390/ijgi9020109

Abstract

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas.

Highlights

The aggravated urbanization and expansion of cities reflect the impact of human activities on the natural environment
Research on urban land use using remote sensing can reflect the relationship among economic development, human activity, and the natural environment [1]
Traditional Synthetic Aperture Radar (SAR) image information is extracted by using the difference in the backscatter intensity of the target [3], but it is difficult to solve the problem of the same spectrum of foreign matter

Summary

Introduction

The aggravated urbanization and expansion of cities reflect the impact of human activities on the natural environment. Research on urban land use using remote sensing can reflect the relationship among economic development, human activity, and the natural environment [1]. With improvements in SAR image resolution, detailed information of the image is obvious, and texture features of the building area are more abundant and applied to the information extraction of a high-resolution SAR image. GAO, and Kuang [4] used the variation function to calculate the texture features of SAR images and applied the unsupervised fuzzy mean classification method to extract the building area. High-resolution polarimetric synthetic aperture radar (referred to as PolSAR) images can obtain ground scene information from multiple dimensions, on the one hand to provide rich information for identification of features and on the other hand to increase the complexity of information extraction. In the context of big data, rapid and accurate automatic extraction technology is the future development trend and is of great significance in promoting the application of PolSAR

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Results

Conclusion