SAR image change detection method based on multi-region convolutional neural network

Abstract

An SAR image change detection method based on a multi-region convolutional neural network comprises the following steps: performing difference analysis on two SAR images with different time phases atthe same geographic position to obtain a difference image; pre-classifying the differential images to obtain a constructed training data set and a constructed test data set; sending the sample training data set into a proposed multi-region convolutional neural network for training; and testing the trained network the test set so as to obtain the change detection result of the whole same-ground multi-temporal SAR image. According to the method, when the data set is constructed, the number of the data set is doubled by adding Gaussian noise, the diversity of samples is enriched, and the overfitting problem is solved; at the same time, the method uses an attention mechanism for channels and spaces to improve the performance of the network, improves the robustness of SAR image change detectionto noise, and has strong generalization ability.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a SAR (Synthetic Aperture Radar, Synthetic Aperture Radar) image change detection method based on a multi-regional convolutional neural network. The invention mainly utilizes some related technologies in the field of image processing and deep learning, Being able to detect changes in ground features in multi-temporal SAR images is of great significance in the fields of natural disaster detection and assessment, urban planning, and land use. Background technique [0002] Image change detection techniques aim to detect changes that occur between images of the same scene at different time periods. Image change detection techniques mainly rely on changes in radiance values ​​or local textures. These changes may be due to real changes in land cover, or by illumination angle, atmospheric conditions, sensor accuracy. Caused by changes in conditions such as ground ...

AI Technical Summary

Problems solved by technology

(1) The accuracy of the unsupervised method greatly depends on the data distribution of the image. If the data distribution is reasonable, the traditional threshold segmentation and clustering methods can obtain better results, but the noise robustness and adaptability of the unsupervised method poor

(2) Supervised methods can often achieve more effective results, such as restricted Boltzmann machines, extreme learning machines, convolutional neural networks and other learning models, but supervised learning methods require a large number of labeled samples for model training. It is difficult to achieve excellent performance when the label quality is poor and the number is insufficient. In addition, the generalization ability of the model will be greatly affected due to noise

In conclusion, when performing change detection on multi-temporal SAR images, the current method is easily affected by noise, and it is difficult to obtain accurate change information

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