Remote Sensing Identification Method for Open-Pit Coal Mining Area Based on Improved Faster-RCNN

Using satellite remote sensing technology coupled with deep learning algorithms could characterize open-pit coal mining area dynamically and efficiently. This study focused on typical open-pit coal mining areas from China and other major coal-resource countries as research objects. Based on GF-2 multi-spectral remote sensing images, data sets and labels are produced to construct faster-regions with convolutional neural networks(CNN) features target recognition model. Low-resolution semantic layer and high-resolution texture information of mining area and background area were fully mined by incorporating the feature Pyramid network, which could improve faster-regions with CNN features model by optimizing parameters. The results showed that the average detection accuracy was improved to 98. 48%, and the overall recognition accuracy reached 96. 7% . The improved faster-regions with CNN features model efficiently increases the identification accuracy of multi-scale and multi-type open-pit mining targets in complex backgrounds. These findings can provide a scientific and accurate technical way for global energy cooperation, environmental protection, and mineral resources utilization in China. © 2023 Northeast University. All rights reserved.