Abstract
Abstract. Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It’s of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.
Highlights
The emergence of airborne high-resolution SAR image enriches the texture information in the building area, it improves the complexity of the environment of the building area
Weight is a part of the proportion of the contribution to the overall contribution, from its essence, this paper proposed the feature weight determination method combined with Bhattacharyya distance, in which bus distance formula is[4]: BD
In order to illustrate the advantages of the method, in this paper, two sets of contrast tests are conducted, which are only considered the statistical features and the structural features, and the classification results are obtained as Figure 6 (c) and 6 (d)
Summary
The emergence of airborne high-resolution SAR image enriches the texture information in the building area, it improves the complexity of the environment of the building area. The texture classification method is the main method to extract the architectural area from the SAR image. There are four main methods for texture feature description, which are statistical-based method, structure-based method, model-based method and filteringbased method. Zhao Ling-jun et al proposed a method for extracting built-up areas from high-resolution SAR images based on variogram textural feature[2]. Tison C et al revealed a new statistical model for Markovian classificati-on of urban areas in high-resolution SAR images[4]. In the feature selection, the direct abandonment of the contribution of the smaller features, will inevitably lose part of the information On this basis, a method of weighted fusion of comprehensive statistics and structure feature is proposed in this paper, and the built-up areas extraction from high resolution SAR image is realized
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