Unsupervised change detection method and system for homologous or heterologous remote sensing image

Abstract

The invention discloses an unsupervised change detection method and system for a homologous or heterologous remote sensing image; the method comprises the steps: inputting a homologous or heterologous dual-time-phase image into a twin full-convolution feature extraction network, and obtaining feature difference images of different scales; constructing a feature fusion network which corresponds to each scale and is based on meta learning and grouping convolution, and fusing the feature difference graphs of different scales to obtain a difference graph; using an improved objective function to complete optimization of the twin full convolution feature extraction network and the feature fusion network based on meta learning and packet convolution, and using an Adam optimization method to iteratively update weight values of the twin full convolution feature extraction network and the feature fusion network; in the training process, continuously increasing the numerical value difference between the variable pixels and the invariable pixels in the difference image, and completing binarization operation on the trained difference image through a threshold value to obtain a binary change image with the same size as the original image. According to the method, a change detection result of a pair of homologous or heterologous dual-temporal remote sensing images can be efficiently and accurately obtained in an unsupervised mode.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an unsupervised change detection method and system for homologous or heterogeneous remote sensing images, which can be used to accurately detect changes in remote sensing images obtained from the same or different sensors at two different times Changes that occur between two images and generate corresponding change detection maps. Background technique [0002] With the increase in the types and numbers 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 sate...

AI Technical Summary

Problems solved by technology

However, how to fully mine the multi-scale deep features between image pairs, reasonably select and use the extracted features, and highlight and emphasize the difference information between image pairs are the difficulties faced by the current change detection task.

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