Optimal Allocation Algorithm of Geological and Ecological High-resolution Remote Sensing Monitoring Sampling Points

The purpose of this study is to solve the problem of unsatisfactory image representation of monitoring sampling points in high-resolution remote sensing due to the complexity of geological ecology. Firstly, three algorithms used in remote sensing technology were introduced, that is, extraction algorithm of monitoring sampling point (selective search algorithm), discriminant algorithm (support vector machine) and BING algorithm. Then, the BING algorithm was improved. Finally, the superiority of the improved BING algorithm was verified through experimental data set. The results showed that selective search algorithm could generate more candidate windows in remote sensing image and had better adaptability. The improved algorithm had higher quality of candidate windows extracted from remote sensing images. Although the IBING algorithm could greatly improve the extraction speed of remote sensing, the detection time of each image became larger. Such testing times were still acceptable. Therefore, in this research, the allocation algorithm of geological and ecological high-resolution remote sensing monitoring sampling points was optimized, which had a good guiding significance for the application of remote sensing technology in geological and ecological research.