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.
Highlights
The ground always shifts vertically and horizontally
Our convolutional neural network (CNN) is designed to improve Interferometric Synthetic Aperture Radar (InSAR) analysis by mutually incorporating the InSAR image and the tropospheric delay error, which are estimated by any conventional methods
The Global Navigation Satellite Systems (GNSSs) including Global Positioning System (GPS), GLObal NAvigation Satellite System (GLONASS), Galileo, and Quasi-Zenith Satellite System (QZSS) measure the crustal alteration so that ground-installation devices receive signals transmitted from artificial satellites
Summary
The ground always shifts vertically and horizontally. One of the critical shifts is a land subsidence. From the phase shift between those captured at different capturing times, we can compute the ground subsidence As summarized, while both the leveling and GNSS (1) require special measurement devices and (2) measure the amount of the land subsidence only at specific points, InSAR analysis can observe a wide area in short time intervals with no ground-installation device. While both the leveling and GNSS (1) require special measurement devices and (2) measure the amount of the land subsidence only at specific points, InSAR analysis can observe a wide area in short time intervals with no ground-installation device These advantages motivate us to employ InSAR analysis for various real-world applications (e.g., [1]–[4]). We explore several types of multi-modal fusion approaches for our proposed CNN in order to evaluate the appropriateness of each approach
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call