Built-Up Area Extraction Using High-Resolution SAR Images Based on Spectral Reconfiguration

Abstract

Built-up area extraction is a primary and fundamental processing in applications such as city planning using remotely sensed high-resolution synthetic aperture radar (SAR) images. One of the critical challenges is that canopy-covered area is always falsely extracted due to similarity between canopy and building in scattering power and texture pattern, resulting in high false alarm and low overall accuracy. In this letter, physical scatterings on built-up areas and canopy-covered areas are analyzed, seeking the distinct differences between these two ground types under various observing scales. A spectral reconfiguration (SR) descriptor is proposed in frequency domain to describe differences that can be strengthened by frequency modulating strategy. Both theoretical and experimental analyses show that the SR descriptor can separate buildings and canopy greatly. Meanwhile, it enjoys both slight computational burden and low operative complexity. Based on this descriptor, an SR-intensity-based built-up extraction algorithm is proposed. Experimental results validate that the SR-intensity-based algorithm acquires low false alarm rate with high accuracy, showing great practical meaning and potential of improvement for the application of urban remote sensing.