Heterogeneous image change detection method and device based on convolutional neural network fusion

Abstract

The invention discloses a heterogeneous image change detection method and device based on convolutional neural network fusion, wherein the method includes the following steps: extracting the convolutional neural network structure and homogeneous transformation features of heterogeneous images; Loss function, which fuses the content features and style features between heterogeneous images to perform homogeneous space transformation of heterogeneous images, and obtain the result of homogeneous space transformation; perform change detection according to the results of homogeneous space transformation, and obtain the final heterogeneity Quality image change detection results. This method can still accurately detect the changing regions of heterogeneous images under complex terrain conditions, and then comprehensively and deeply extract the homogeneous transformation features between heterogeneous images.

Description

technical field [0001] The invention relates to the technical field of remote sensing image fusion target detection, in particular to a method and device for detecting changes in heterogeneous images based on convolutional neural network fusion. Background technique [0002] Related technologies, (1) Earthquake damage assessment of buildings using VHRoptical and SAR imagery, the pioneering work of change detection for scenes of heterogeneous remote sensing images, here, the scene of heterogeneous remote sensing images refers to the images before and after the change are heterogeneous , there is no additional homogeneous remote sensing image for fusion change detection. This paper creatively proposes a method to construct a homogeneous feature space, that is, to map heterogeneous images into homogeneous images for change detection. Specifically, the terrain parameters of the optical image before the change are extracted, and the SAR image before the change is synthesized acc...

AI Technical Summary

Problems solved by technology

These complex image backgrounds and truly changing image regions are easily confused in the features extracted by traditional change detection algorithms, which in turn lead to detection errors

[0011] To sum up, in related technologies, there are limitations of heterogeneous image detection algorithms in homogeneous space construction in complex terrain environments, and it is difficult to fully and accurately perform homogeneous space transformation under complex terrain conditions, which needs to be solved urgently

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