Extraction of domestic satellite images patches based on deep learning
This paper addresses the characteristics of domestic satellite imagery,such as multi-temporal,long-time series,massive,and massive multi-source data,proposes an efficient and accurate method for the extraction of satellite imagery patches.Based on the principles of deep learning,this method constructs a semantic segmentation model for ground objects and a group of intelligent algorithms for patch extraction based on deep learning theory,enabling the automatic recognition of the features,patterns,and attributes of satellite imagery patches,which leads to the intelligent and automated extraction of these patches.Experimental results demonstrate that this method achieves a high level of accuracy in the extraction of patches from domestic satellite imagery,provides important support for subsequent image processing,analysis,and applications.
deep learningdomestic satellite imagesspot extraction
万方数据
维普
