Unsupervised remote sensing image change detection method, storage medium and computing device

Abstract

The invention discloses an unsupervised remote sensing image change detection method, a storage medium and computing equipment. The method comprises the following steps: constructing a multi-scale image convolutional neural network; respectively inputting the dual-temporal images into a multi-scale image convolutional neural network, extracting spatial features and inter-spectrum features, and jointly calculating to generate an initial pseudo label; cascading the two images of the dual-temporal image, inputting the two images into a multi-scale image convolutional neural network, and trainingthe multi-scale image convolutional neural network to generate a two-channel difference image; utilizing a metric learning module of the multi-scale graph convolutional neural network to update the initial pseudo label as a label of a two-channel difference graph, and training the generated two-channel difference graph; and comparing two channels of the trained two-channel difference image to obtain a binary change image with the same size as the original image, and completing image change detection. According to the method, the change detection graph of the pair of dual-temporal images can beefficiently and accurately obtained in an unsupervised manner.

Description

technical field [0001] The invention belongs to the technical field, and in particular relates to an unsupervised remote sensing image change detection method, storage medium and computing device based on multi-scale graph convolution and metric learning, which can be used to accurately detect Detect changes that occur between two images and generate corresponding change detection maps. Background technique [0002] With the increase in the number of remote sensing satellites and the improvement of earth observation technology in recent years, people can have more opportunities to monitor changes in the earth's surface from space, and the task of remote sensing image change detection has thus emerged. This task can play a crucial role in practical applications such as land cover monitoring, disaster management, ecosystem monitoring, urban planning, etc. In order to achieve change detection, various satellite platforms have provided a large number of multi-temporal remote se...

AI Technical Summary

Problems solved by technology

The disadvantage is that only traditional feature extraction methods are used for feature extraction of remote sensing images. This process is usually insufficient, and only local information can be extracted, lacking global information and depth information, resulting in poor detection accuracy. high

The disadvantage is that it relies too much on the pre-trained convolutional neural network, which cannot guarantee the complete extraction of the features that are really suitable for change detection in the dual-temporal image pair, resulting in low detection accuracy.

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