Automatic Building Detection for Multi-Aspect SAR Images Based on the Variation Features

Abstract

Multi-aspect synthetic aperture radar (SAR) images contain more information available for automatic target recognition (ATR) than images from a single view. However, the sensitivity to aspect angles also makes it hard to extract and integrate information from multi-aspect images. In this paper, we propose a novel method based on the variations features to realize automatic building detection in the image level. First, to get a comprehensive description of target variation patterns, statistical characteristic variances are derived from three representative and complementary categories. Then, these obtained features are fused and put in the K-means classifier for prescreening, whose results are used as the training sets in supervised classification later to avoid manual labeling. Second, for more precise detection performance, finer features in vector forms are obtained by principal component analysis (PCA). The variation patterns of these feature vectors are explored in two different manners of correlation and fluctuation analyses and processed by separate support vector machines (SVMs) after fusion. Finally, the independent SVM detection results are fused according to a maximum probability rule. Experiments conducted on two different airborne data sets demonstrate the robustness and effectiveness of the proposed method, in spite of significant target signature variabilities and cluttered background.

A Novel Method for Building Contour Extraction Based on CSAR Images

“…Remote Sens. 2023, 15, x FOR PEER REVIEW 3 of 22 areas in complex scenes [23]. However, this method focuses on the extraction of changing areas; it cannot suppress the changing clutter near the buildings, so the extracted area boundaries are not accurate.…”

“…For example, Yue et al used statistical distribution and membership to extract anisotropic scattering features and amplitude features of man-made targets, respectively [22], but the statistical distribution parameters of this method are difficult to estimate, and the sliding window size of the distribution model needs to be selected manually. Liu et al first extracted multiple feature variances to obtain a comprehensive description of the target change pattern, then obtained finer feature vectors through principal component analysis, and finally used support vector machines to achieve automatic extraction of building areas in complex scenes [23]. However, this method focuses on the extraction of changing areas; it cannot suppress the changing clutter near the buildings, so the extracted area boundaries are not accurate.…”

Individual High-Rise Building Extraction from Single High-Resolution SAR Image Based on Part Model

“…The first is to extract the building distribution area. By analyzing the statistical characteristics of building SAR amplitude images [9][10][11][12], or learning features of build-up areas through neural networks [13,14], we can determine whether a certain pixel belongs to the building area or not. This method can produce macro-statistics on the development trend of the city, and can also be used for change detection.…”