Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm

• Published in: Applied sciences Vol. 8; no. 11; p. 2206
• Main Authors: Chen, Xiaohong, Sun, Qian, Hu, Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2018
• Subjects: active distortion; Algorithms; DEM; InSAR; Investigations; landslides; Landslides & mudslides; Looting; neighbor gradient; passive distortion; Remote sensing; Sensors; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app8112206

Radar-specific imaging geometric distortions (including foreshortening, layover, and shadow) that occur in synthetic aperture radar (SAR) images acquired over mountainous areas have a negative impact on the suitability of the interferometric SAR (InSAR) technique to monitor landslides. To address this issue, many distortion simulation methods have been presented to predict the areas in which distortions will occur before processing the SAR image. However, the layover and shadow regions are constituted by active as well as passive subregions. Since passive distortions are caused by active distortions and can occur in the flat area, it is difficult to distinguish the transition zone between passive distortion and non-distortion areas. In addition, passive distortion could cover part of the foreshortening or active layover/shadow areas but has generally been ignored. Therefore, failure to simulate passive distortion leads to incomplete simulated distortions. In this paper, an algorithm to define complete SAR geometric distortions and correct the boundaries among different distortions is presented based on the neighbor gradient between the passive and active distortions. It is an image-processing routine applied to a digital elevation model (DEM) of the terrain to be imaged by the available SAR data. The performance of the proposed method has been validated by the ascending and descending Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) images acquired over the Chongqing mountainous area of China. Through the investigation of passive distortion, we can have a deeper understanding of the formation and characteristics of these distortions. Moreover, it provides very meaningful information for research on areas such as landslide monitoring.