Integrating Incidence Angle Dependencies Into the Clustering-Based Segmentation of SAR Images

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Cristea, Anca, Van Houtte, Jeroen, Doulgeris, Anthony Paul
• Format: Article in Journal/Newspaper
• Language: English
• Published: Institute of Electrical and Electronics Engineers (IEEE) 2020
• Subjects: VDP::Technology: 500; VDP::Teknologi: 500; Arctic; Sea ice
• Online Access: https://hdl.handle.net/10037/21036; https://doi.org/10.1109/JSTARS.2020.2993067

Synthetic aperture radar systems perform signal acquisition under varying incidence angles and register an implicit intensity decay from near to far range. Owing to the geometrical interaction between microwaves and the imaged targets, the rates at which intensities decay depend on the nature of the targets, thus rendering single-rate image correction approaches only partially successful. The decay, also known as the incidence angle effect, impacts the segmentation of wide-swath images performed on absolute intensity values. We propose to integrate the target-specific intensity decay rates into a nonstationary statistical model, for use in a fully automatic and unsupervised segmentation algorithm. We demonstrate this concept by assuming Gaussian distributed log-intensities and linear decay rates, a fitting approximation for the smooth systematic decay observed for extended flat targets. The segmentation is performed on Sentinel-1, Radarsat-2, and UAVSAR wide-swath scenes containing open water, sea ice, and oil slicks. As a result, we obtain segments connected throughout the entire incidence angle range, thus overcoming the limitations of modeling that does not account for different per-target decays. The model simplicity also allows for short execution times and presents the segmentation approach as a potential operational algorithm. In addition, we estimate the log-linear decay rates and examine their potential for a physical interpretation of the segments.