Remote sensing image classification method, storage medium and computing equipment

Abstract

The invention discloses a remote sensing image classification method, a storage medium and computing equipment, and the method comprises the steps: building a remote sensing image set, carrying out the standardization of the remote sensing image set, and obtaining a training sample set and a test sample set; setting a multi-scale feature extraction module, and generating feature maps of two scalesby setting different hole convolution in two parallel convolution modules; setting a self-adaptive feature fusion module, wherein the self-adaptive feature fusion module can adaptively select usefulinformation in the two generated features of different scales and perform fusion; building a whole neural network model; performing iterative training on the whole neural network model by using the training sample set; and randomly selecting a sample from the test samples as a position category sample, and classifying unknown samples needing to be predicted by using the trained neural network. According to the method, redundant information is reduced, multi-scale features are selected more flexibly, the stability of the network is improved, and then the classification capacity of the network model is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a remote sensing image classification method, a storage medium and a computing device based on a double-branch deep multi-scale network. Background technique [0002] Remote sensing image scene classification is a basic remote sensing image processing task, which is widely used in military and civilian fields. In recent years, remote sensing technology has developed rapidly, and the captured remote sensing images have the characteristics of high resolution and complex structure. Traditional remote sensing image processing methods are difficult to capture the semantic information in complex images, so they do not perform well in the classification tasks of current high-quality images. [0003] In recent years, deep learning has developed rapidly and has achieved good results in the field of image processing. Thanks to volume and operation and hierarchical st...

AI Technical Summary

Problems solved by technology

However, the current convolutional neural network model has certain limitations. Since the general model has only a fixed receptive field, it is difficult to extract multi-scale features. When faced with changes in the size of the target in the image, it is often difficult for the model to fully capture the information of the target.

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