Land Cover Classification of Open-pit Mining Areas Based on Improved DeepLabv3+and High-resolution Images
Deep learning technology and high-resolution remote sensing images provide efficient technical means for the classification of land cover in mining areas. Aiming at the problems of inaccuracy,omission and commission of feature boundary segmentation in the classification of land cover in mining areas,this paper improves the original DeepLabv3+deep learning model by means of feature fu-sion in coding layer,adding supervision branch module in backbone network and merging shallow features in decoding layer. The ex-perimental results show that the mIoU value of the improved DeepLabv3+model is 80.32％ on average,which is 5.71％ higher than that of the original DeepLabv3+model. It can effectively classify the land cover of open-pit mining areas.
deep learningsemantic segmentationopen-pit mining areashigh-resolution images
万方数据
维普
