Multi-Modal Data Fusion for Land-Subsidence Image Improvement in PSInSAR Analysis

Abstract

There are three popular methods to understand the land subsidence: leveling, Global Navigation Satellite System, and Interferometric Synthetic Aperture Radar (InSAR) analysis using SAR images. While both leveling and the Global Navigation Satellite System can measure the amount of land subsidence only at specific points, InSAR analysis can observe a wide area in short time intervals. In terms of accuracy, however, InSAR analysis is inferior to leveling; centimeter/millimeter order (InSAR/PSInSAR analysis) vs. millimeter order (leveling). Among all observation errors in InSAR analysis, a tropospheric delay error has a large adverse effect on the measurement. It is difficult to suppress this tropospheric delay error by conventional methods because they try to remove error at each pixel independently in an InSAR image. However, geometrically-neighboring regions/pixels should be naturally correlated. Our proposed method employs such a neighboring relationship in a convolutional neural network (CNN). Our CNN is designed to improve InSAR analysis by mutually incorporating the InSAR image and the tropospheric delay error, which are estimated by any conventional methods. Experimental results demonstrate that our proposed method can reduce the mean error compared with a conventional method: from 10.3mm to 6.80mm.