scholarly journals Fast SAR Image Change Detection Using Bayesian Approach Based Difference Image and Modified Statistical Region Merging

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Han Zhang ◽  
Weiping Ni ◽  
Weidong Yan ◽  
Hui Bian ◽  
Junzheng Wu
Keyword(s):  
Change Detection ◽  
Bayesian Approach ◽  
Generation Process ◽  
Sar Image ◽  
Region Merging ◽  
Difference Image ◽  
Leibler Divergence ◽  
Image Change Detection ◽  
Log Ratio ◽  
Statistical Region Merging

A novel fast SAR image change detection method is presented in this paper. Based on a Bayesian approach, the prior information that speckles follow the Nakagami distribution is incorporated into the difference image (DI) generation process. The new DI performs much better than the familiar log ratio (LR) DI as well as the cumulant based Kullback-Leibler divergence (CKLD) DI. The statistical region merging (SRM) approach is first introduced to change detection context. A new clustering procedure with the region variance as the statistical inference variable is exhibited to tailor SAR image change detection purposes, with only two classes in the final map, the unchanged and changed classes. The most prominent advantages of the proposed modified SRM (MSRM) method are the ability to cope with noise corruption and the quick implementation. Experimental results show that the proposed method is superior in both the change detection accuracy and the operation efficiency.

scholarly journals SAR Image Change Detection Based on Multi-scale Feature Extraction

2021 ◽  
Vol 10 (4) ◽  
pp. 077-081
Author(s):  
Xiaoqian Yuan ◽  
Chao Chen ◽  
Shan Tian ◽  
Jiandan Zhong
Keyword(s):  
Feature Extraction ◽  
Change Detection ◽  
Speckle Noise ◽  
Kernel Matrix ◽  
Sar Image ◽  
Scale Feature ◽  
Difference Image ◽  
Multi Scale ◽  
Image Change Detection ◽  
The Difference

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

Using Combined Difference Image and $k$ -Means Clustering for SAR Image Change Detection

2014 ◽  
Vol 11 (3) ◽  
pp. 691-695 ◽  
Author(s):  
Yaoguo Zheng ◽  
Xiangrong Zhang ◽  
Biao Hou ◽  
Ganchao Liu
Keyword(s):  
Change Detection ◽  
Sar Image ◽  
Difference Image ◽  
Image Change Detection

scholarly journals A Convolutional Neural Network with Parallel Multi-Scale Spatial Pooling to Detect Temporal Changes in SAR Images

2020 ◽  
Vol 12 (10) ◽  
pp. 1619 ◽  
Author(s):  
Jia-Wei Chen ◽  
Rongfang Wang ◽  
Fan Ding ◽  
Bo Liu ◽  
Licheng Jiao ◽  
...  
Keyword(s):  
Change Detection ◽  
Sar Image ◽  
Sar Images ◽  
Convolutional Network ◽  
Difference Image ◽  
Multi Scale ◽  
Spatial Pooling ◽  
Testing Dataset ◽  
Image Change Detection ◽  
The Difference

In synthetic aperture radar (SAR) image change detection, it is quite challenging to exploit the changing information from the noisy difference image subject to the speckle. In this paper, we propose a multi-scale spatial pooling (MSSP) network to exploit the changed information from the noisy difference image. Being different from the traditional convolutional network with only mono-scale pooling kernels, in the proposed method, multi-scale pooling kernels are equipped in a convolutional network to exploit the spatial context information on changed regions from the difference image. Furthermore, to verify the generalization of the proposed method, we apply our proposed method to the cross-dataset bitemporal SAR image change detection, where the MSSP network (MSSP-Net) is trained on a dataset and then applied to an unknown testing dataset. We compare the proposed method with other state-of-arts and the comparisons are performed on four challenging datasets of bitemporal SAR images. Experimental results demonstrate that our proposed method obtain comparable results with S-PCA-Net on YR-A and YR-B dataset and outperforms other state-of-art methods, especially on the Sendai-A and Sendai-B datasets with more complex scenes. More important, MSSP-Net is more efficient than S-PCA-Net and convolutional neural networks (CNN) with less executing time in both training and testing phases.

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