Saliency and Background Prior-Based Residential Area Detection for SAR Images
Libao Zhang, Shiyi Wang · IEEE Geoscience and Remote Sensing Letters · 2019
Due to the lack of color, the strong speckle noise, and the complex background clutter, target detection in synthetic aperture radar (SAR) images is a challengeable task. A novel saliency and background prior (SBP)-based residential area detection method for SAR images is proposed in this letter. It has three major advantages compared with other methods: 1) in saliency analysis, it deeply exploits the image feature and conceives a new texture representation using the amplitude of partitioning Fourier transform (pFT), which compensates for the lack of color and spectrum information in SAR; 2) it employs the superpixel-level background prior and monitors the average intensity level (AIL) of each superpixel for generating accurate outlines of residential areas; and 3) two regional feature-based indices are presented to select the background clutter, and the results serve as a modification to saliency analysis. Experiments using ALOS PALSAR images show that the proposed method has great priority in both quality and quantity over competing methods by extracting integrated residential areas with clear boundaries.