NEURAL NETWORK OPTIMIZATION METHOD FOR REMOTE SENSING IMAGE CLASSIFICATION, AND TERMINAL AND STORAGE MEDIUM

摘要

The present application relates to a neural network optimization method for remote sensing image classification, and a terminal and a storage medium. The method comprises: acquiring a remote sensing image data set; constructing an anti-noise network model, wherein the anti-noise network model comprises an image segmentation model and a loss selection model, and the image segmentation model is a U-Net network based on an SE module; and inputting the remote sensing image data set into the anti-noise network model for iterative training, performing, via the anti-noise network model, image segmentation by means of the U-Net network based on the SE module, so as to obtain an image classification result, using, via the loss selection model, a ksigma criterion to perform loss selection, and removing an error that exceeds a set deviation interval, so as to obtain an optimal network model parameter. By means of the embodiments of the present application, the feature extraction capability of a network model is improved, and the problem of a decrease in the classification precision of a neural network caused by noise of tags in a remote sensing image data set is solved.