Algorithmic Research on Hydrogeological Comprehensive Monitoring Using Multi-Source Remote Sensing Data

In response to the difficulties in obtaining data and high-error data in current hydrogeological surveys, this article uses high-resolution satellite image processing to capture wastewater characteristics based on the principle of remote sensing reflectance. The automatic threshold selection algorithm determines the optimal threshold for extracting wastewater information and establishes a model for identifying wastewater, achieving the purpose of better identification of water quality. This article uses drones to capture geological images for processing. Based on two-dimensional wavelet transform, remote sensing images are divided into four sub bands and filtered. The four sub bands can be input into the residual network learning algorithm to obtain high-quality four new sub bands, and finally transformed into a complete high-quality remote sensing geological image through inverse two-dimensional wavelet transform, achieving the purpose of better identification of water quality. The results show that the wastewater recognition model can accurately identify wastewater according to the characteristics that the reflectance of wastewater water in blue. Green and red wavelengths is lower than that of normal water, while the reflectance in infrared wavelengths is higher than that of normal water. The peak signal-to-noise ratio and mean square error of the residual learning network algorithm using two-dimensional wavelet transform for geological remote sensing images are 28.12 and 0.051, which are the best compared with the other two methods, indicating that the image quality after processing is high.