A Change Detection Method for SAR Image Based on Stacked Semi-Supervised Adaptive Denoising Autoencoder

Abstract

The invention discloses a SAR image change detection method based on a stacked semi-supervised self-adaptive denoising self-encoder, which solves the problem of low detection accuracy of coherent speckle noise points and many edge change regions in the existing method. The implementation steps are: first generate a multi-scale difference guidance map; use the phase 1 image as input to train SDAE; use the multi-scale difference guidance map, phase 1 and phase 2 images as input to train SSADAE, and SSADAE adaptive error function The weights obtained by SDAE training are used in the method; then SSADAE is used to calculate the feature vectors of phase 1 and phase 2 images; the two are subtracted to obtain the difference vector, and then FCM classification is performed on it to obtain the change detection result map. The present invention firstly proposes a multi-scale difference guidance map, which can highlight the change area in the difference map; the SSADAE proposed later can use a small number of marked samples in the image to further improve the accuracy of change detection.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to change detection of SAR images, in particular to a SAR image change detection method based on a stack semi-supervised self-adaptive denoising self-encoder. This method can be used in change detection of SAR images. Background technique [0002] Change detection is one of the key technologies in the field of remote sensing. It detects the changes in the gray value or local texture of images in different periods in the same imaging scene, and obtains the shape, position, quantity and other properties of the surface or objects of interest. change information. It has a wide range of applications in the fields of society, environment and military. [0003] In the multi-temporal SAR image change detection method, there are two main routes, one is post-classification comparison (Post Classification Comparison, PCC), and the other is post-comparison classification. The former me...

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Problems solved by technology

[0005] In 2012, Maoguo Gong and Yu Cao et al. published the article A Neighborhood-Based Ratio Approach for Change Detection in SAR Images in IEEE Geoscience and Remote Sensing Letters, Volume 9, Issue 2, Page 307-311, which proposed a neighborhood-based ratio operator (NR) , the NR operator adds the heterogeneity / homogeneity operator of the image, but when the noise distribution of the two SAR images before and after the change is inconsistent, the detection effect of this method is not accurate enough

Disadvantages of this method: First, the generation of initial change masks for optical images and SAR images will introduce large errors, and the learning samples with large errors will also have a greater impact on the results; second, SDAE uses an unsupervised method To extract features, so the extracted features have a certain degree of randomness, and the feature change analysis based on the mapping proposed by the author is completely dependent on the features extracted by SDAE, which will further introduce errors

[0008] To sum up, when the image noise distribution before and after the change is inconsistent, the above method is not good enough for edge detail detection, and the overall error rate of change detection is high

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