Journal ArticleDOI
SAR image change detection based on deep denoising and CNN
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TLDR
An end-to-end deep denoising model is designed to remove the noise of SAR images with the help of abundant simulated SAR images, and this model is trained effectively to estimate the noise component.Abstract:
The intrinsic noise of synthetic aperture radar (SAR) images has a big influence to the image processing performance, especially in change detection (CD). Image denoising is an important branch of image restoration which aims at enhancing the quality of images. The detection accuracy of CD depends greatly on the quality of red difference image (DI), therefore image denoising can be regarded as a vital step in SAR CD. However, few researches focused on this problem. In this study, an end-to-end deep denoising model is first designed to remove the noise of SAR images. With the help of abundant simulated SAR images, deep denoising model is trained effectively to estimate the noise component. Then clean image can be achieved by removing this noise component from the original SAR image. After denoising, the new image pair will generate a clean DI. At last, DI is classified into changed and unchanged areas by a three-layer Convolutional Neural Network (CNN). Three real SAR image pairs demonstrate the effectiveness of the proposed method.read more
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Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis
Lazhar Khelifi,Max Mignotte +1 more
TL;DR: A comprehensive review and a meta-analysis of the recent progress in change detection DL studies for remote sensing images and the fundamentals of deep learning methods which are frequently adopted for change detection are introduced.
Journal ArticleDOI
A Survey on the Applications of Convolutional Neural Networks for Synthetic Aperture Radar: Recent Advances
TL;DR: In this article , major sub-areas of SAR data analysis that have been tackled by CNNs are systematically reviewed, such as automatic target recognition, land use and land cover classification, segmentation, change detection, object detection, and image denoising.
Journal ArticleDOI
A Classified Adversarial Network for Multi-Spectral Remote Sensing Image Change Detection
TL;DR: A deep neural network, named a classified adversarial network (CAN), is proposed for multi-spectral image change detection, based on generative adversarial networks (GANs), which can facilitate the generator learning the transformation from a bitemporal image to a change map.
Journal ArticleDOI
SAR image despeckling using deep CNN
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