Land Cover Classification Method Based on Improved Convolutional Neural Network
Convolutional neural networks have insufficient refining ability for global contextual information in high-resolution image classification,resulting in lower classification accuracy.In response to this issue,the article proposes a land cover classification method based on an improved convolutional neural network.This method utilizes a backbone network based on self attention modules to enhance the ability to aggregate global contextual information,achieving better segmentation results than other comparative networks in land cover datasets.
improve convolutional neural networksclassification of surface coverremote sensing images
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