Remote sensing image segmentation based on multi-scale feature fuzzy convolutional neural network
In order to solve the uncertainty of"same spectrum of different objects,the same object with different spectrum"and the low utilization rate of large amount of spatial information in high-resolution remote sensing images,a fuzzy convolutional neural net-work model based on multi-scale features was proposed.The fuzzy learning module was added to the long jump connection to re-move noise features and ease the uncertainty between classes.The multi-scale features were fused by atrous spatial pyramid pooling,and complete spatial context information was extracted to improve segmentation performance.The experimental results showed that the overall accuracy of the model on Potsdam dataset and Vaihingen dataset reached 92.65%and 93.19%,respectively,which were significantly better than the existing popular deep learning models and could significantly improve the semantic segmentation per-formance of high-resolution remote sensing images.
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