Heterogeneous image change detection method based on coupled translation network

Abstract

The invention discloses a heterogeneous image change detection method based on a coupling translation network, which mainly solves the problems of low precision and weak robustness of the existing heterogeneous image change detection method. The implementation steps are: 1) Set the structure and parameters of the two translation networks; 2) Input two heterogeneous images and calculate the Jason-Shannon divergence distance between the two images and the probability coefficient of the pixel unchanged; 3) Train the first translation network to obtain the translation result image of the first image; 4) train the second translation network to obtain the translation result image of the second image; 5) update the probability that the pixel has not changed according to the two translation result images coefficient; 6) Repeat steps 3)-5) in turn until the network objective function value is stable; 7) Obtain the difference map according to the two translation result maps; 8) Cluster the difference map to obtain the final change detection map. The invention has the advantages of accurate detection and strong robustness, and can be used for image translation, pattern recognition and target tracking.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a heterogeneous image change detection method, which can be used for image generation, pattern recognition or target tracking. Background technique [0002] Change detection is a technique for detecting changes in an area by analyzing a set of images taken at different times in the same location. According to different image sources, change detection can be divided into homologous image change detection and heterogeneous image change detection. Among them, homologous images refer to images taken by the same sensor, which have the same attributes, and the pixels in the unchanged area are linearly correlated, so that the difference between pixels can be directly compared to obtain a difference map; heterogeneous images are images obtained by different sensors, For example, synthetic aperture radar SAR images and optical images, their different statistical properties betwe...

AI Technical Summary

Problems solved by technology

However, this method only calculates the unchanged area and uses a fixed parameter to judge whether the pixel has changed or not, resulting in low accuracy when dealing with images with many changed areas or multiple targets.

[0004] Since the above-mentioned classification-based heterogeneous image change detection methods are not accurate and require human intervention, the unsupervised convolutional neural network-based method has a small application range

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