SAR image change detection system and method based on sparse auto-encoder and convolution neural network

Abstract

The invention proposes a SAR image change detection system and a method based on a sparse auto-encoder and a convolution neural network, which belong to the field of SAR image processing. The method comprises: extracting characteristics from difference graphs through the sparse auto-encoder (SAE) and then using the FCM clustering to perform difference graph clustering according to the image characteristics to obtain an initial classification result; in combination with the difference graphs and the initial classification result, training the convolution neural network; and through the well-trained CNN, fine-tuning the initial classification result to obtain a final classification result graph. This method makes full use of the characteristics information and the neighborhood information of image pixels to further improve the accuracy of the change detection results. The simulation result shows that compared with the traditional algorithms such as KI and FCM, the SAR image change detection method based on the sparse auto-encoder and the convolution neural network obviously has a marked increase in detection efficiency and the Kappa coefficient as well.

Description

technical field [0001] The invention belongs to the field of SAR image processing and relates to change detection of SAR images, in particular to a SAR image change detection system and method based on a sparse autoencoder and a convolutional neural network. Background technique [0002] Synthetic Aperture Radar (SAR) has the incomparable all-weather and all-weather advantages of traditional optical remote sensing, and has gradually become an important data source for change detection. increasingly important role. SAR image change detection is to compare two SAR images in different periods in the same area, and obtain the required ground object change information according to the differences between the images. At present, change detection technology is widely used in many fields, such as forest cover change, urban environment change, especially in natural disaster assessment and post-disaster construction. [0003] At present, there are many methods for image change detec...

AI Technical Summary

Problems solved by technology

[0008] In short, in the change detection methods in the prior art, such as the Kittler&Illingworth (KI) algorithm, Fuzzy C-means (FCM) algorithm, etc., when performing image change detection, only the pixel value information of the image is used for change detection, ignoring or filtering A large amount

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