Coherent pixel selection using a dual-channel 1-D CNN for time series InSAR analysis

作者：Yu Zhang, Jujie Wei, Meng Duan, Yafeng Kang, Qian He, Hongan Wu, Zhong Lu · 发表于：International Journal of Applied Earth Observation and Geoinformation · 年份：2022 · DOI：10.1016/j.jag.2022.102927 · 被引用次数：8 · 研究领域：Synthetic Aperture Radar (SAR) Applications and Techniques、Advanced SAR Imaging Techniques、Structural Health Monitoring Techniques

Coherent pixel (CP) selection is an important step in the processing chain of time series InSAR analysis. In this research, we propose a light deep learning framework, i.e., a dual-channel one-Dimensional Convolution Neural Network (1-D CNN) to select CPs. The 1-D CNN has simple input: SAR amplitude and interferogram coherence, and can be trained with CP samples generated by traditional thresholding method. In an experiment based on Sentinel-1 temporal images in Tianjin, China, the 1-D CNN substantially outperforms the thresholding method and the StaMPS method in terms of the amount and the quality of selected CPs. Additionally, a new measure is proposed to quantify CP quality, which is very useful when other reference data is unavailable. The proposed 1-D CNN framework on CP selection is reliable and fast, and of great significance in developing automatic time-series InSAR processing system.