Improvement and Experimental Analysis of Remote Sensing Image Edge Detection Algorithm Based on Wavelet Transform and Feature Extraction

The improvement of remote sensing image edge detection algorithm based on wavelet transform and feature extraction is mainly aimed at how to extract edge information more effectively from remote sensing images, and this technology can be used in map rendering, resource management, environmental monitoring and other fields. The research background of this paper is that traditional edge detection methods are often unsatisfactory in processing high noise and high complexity remote sensing images, so more accurate and practical methods are needed. The research significance of this paper lies in the improved algorithm to identify and track surface features more accurately, further improve the information utilization rate of remote sensing images, and finally promote the realization of practical application value in urban planning, disaster assessment and other aspects. According to the experimental data, after applying the remote sensing image edge detection algorithm based on wavelet transform and feature extraction, Party A's satisfaction increased from 72.40% to 92.65%, and the satisfaction vote of the system also accounted for 68.2% of the total number of people. It can be seen that the system can effectively extract more clear and accurate edge information to a certain extent, so as to provide a reliable way for users.