A Novel Method for Building Contour Extraction Based on CSAR Images

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 14; p. 3463
• Main Authors: Zhao, Jia, An, Daoxiang, Chen, Leping
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-07-2023
• Subjects: Accuracy; Algorithms; anisotropic scattering; Anisotropy; building; Buildings; circular SAR; Cluster analysis; Clustering; Contours; Data acquisition; Entropy; Feature extraction; Image processing; Methods; Probability distribution; Radar imaging; Scattering; Sensors; strong scattering; sub-aperture image; Synthetic aperture radar; Vector quantization; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15143463

Circular synthetic aperture radar (CSAR) can obtain more complete scattering characteristics by observing the target with different azimuth angles. Therefore, extracting the complete structure of the target from CSAR images is of great significance for accurate interpretation. At present, the artificial target extraction based on CSAR images mostly uses anisotropic scattering features. For special targets such as buildings, as the walls and the ground form dihedral corner structures, there are also obvious strong scattering features such as double-scattering lines in SAR images. Therefore, combining the strong scattering features of buildings at specific aspects with anisotropic scattering characteristics at different aspects can obtain better extraction results, and how to extract these features accurately and efficiently is the key point. Based on this, this paper proposes a novel method for building contour extraction based on CSAR images. For strong scattering features, a fast fuzzy C-means (FCM) clustering algorithm was used to extract them. For anisotropic scattering features, aspect entropy was used to characterize the anisotropy degree, and K-means clustering was combined to extract. Finally, a more accurate result is obtained by merging the two feature extraction results. In order to verify the effectiveness and practicability of the proposed method, a lot of measured data acquired by the self-developed airborne L-band and Ku-band CSAR systems were processed. The experiments show that, compared with state-of-the-art algorithms, the proposed method can obtain more accurate results in less time.