Layover and shadow regions detection based on superpixel segmentation and multi-information fusion

doi:10.11947/j.AGCS.2022.20220111

Abstract

Abstract: In InSAR signal processing, the continuity of InSAR phase maps is seriously destroyed by layover and shadow regions which results in errors in elevation inversion. In this paper, a multi-information fusion-based superpiexl detection algorithm is proposed to make up for the shortcomings of traditional pixel-by-pixel detection methods based on amplitude and coherence coefficient threshold detection, constant false alarm(CFAR) detection and local frequency detection. Due to the lack of digital elevation model of scene, the accuracy verification of the proposed algorithm in practical applications become impossible. Thus, a frequency domain echo simulation technique is proposed. The proposed algorithm is verified based on an advanced integral equation approximation model(AIEM) for simulating SAR echo data. Simulation data and TH-2 data experiments show that the algorithm can distinguish more than 98% of layover and shadow regions in SAR images. Finally, the DEM products precision and the operation efficiency of phase unwrapping are improved significantly by masking layover and shadow regions during the phase unwrapping process.

Key words: TH-2, layover, shadow, simple linear iterative clustering(SLIC), fast echo simulation

CLC Number:

P236

LIU Peng, LI Zhenfang, LOU Liangsheng, YANG Weiming, WANG Zhen. Layover and shadow regions detection based on superpixel segmentation and multi-information fusion[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(12): 2517-2530.

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