Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - SAR image change detection based on deep denoising and CNN
AU - Cao, X.
AU - Ji, Y.
AU - Wang, L.
AU - Ji, B.
AU - Jiao, L.
AU - Han, J.
N1 - Export Date: 8 August 2019
PY - 2019/7/25
Y1 - 2019/7/25
N2 - 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.
AB - 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.
U2 - 10.1049/iet-ipr.2018.5172
DO - 10.1049/iet-ipr.2018.5172
M3 - Journal article
VL - 13
SP - 1509
EP - 1515
JO - IET Image Processing
JF - IET Image Processing
SN - 1751-9659
IS - 9
ER -