scholarly journals Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1179 ◽  
Author(s):  
Xuemei Lou ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Principal Component ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Sar Images ◽  
Denoising Method ◽  
Difference Image ◽  
Time Operation ◽  
Rof Model ◽  
Difference Images

The explicit solution of the traditional ROF model in image denoising has the disadvantages of unstable results and requiring many iterations. To solve the problem, a new method, ROF model semi-implicit denoising, is proposed in this paper and applied to change detections of synthetic aperture radar (SAR) images. All remote sensing images used in this article have been calibrated by ENVI software. First, the ROF model semi-implicit denoising method is used to denoise the remote sensing images. Second, for the denoised images, difference images are obtained by the logarithmic ratio and mean ratio methods. The final difference image is obtained by principal component analysis fusion (PCA fusion) of the two difference images. Finally, the final difference image is clustered by fuzzy local information C-means clustering (FLICM) to obtain the change regions. The research results show that the proposed method has high detection accuracy and time operation efficiency.

scholarly journals Erratum: Lou, X.; Jia, Z.; Yang, J.; Kasabov, N. Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method. Sensors 2019, 19, 1179

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2314
Author(s):  
Xuemei Lou ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Change Detection ◽  
Sar Images ◽  
Denoising Method ◽  
Rof Model

The authors wish to make the following erratum to this paper [...]

scholarly journals Change Detection in SAR Images using Image Fusion and Supervised Classifier

2020 ◽  
Vol 9 (1) ◽  
pp. 685-687
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Classification System ◽  
Supervised Classification ◽  
Input Image ◽  
Sar Images ◽  
Denoising Method ◽  
Pca Algorithm ◽  
Log Ratio

The paper proposes an approach based on a fusion o bject and a supervised classification system to improve detection f or SAR images. Here we are using CNN denoising method for removing noise in the input image. Then information from first image is processed using mean_ratio operator. Second image is processed by log ratio operator. These two images are fused using PCA algorithm and the output is provided to KNN supervised classifier for finding change detection in the image.

A Power Thresholding Function-based Wavelet Image Denoising Method

2018 ◽  
Vol 62 (1) ◽  
pp. 105061-1050611 ◽  
Author(s):  
Zhidan Yan ◽  
Wenyi Xu ◽  
Chunmei Yang
Keyword(s):  
Image Denoising ◽  
Denoising Method ◽  
Wavelet Image ◽  
Thresholding Function

Adaptive solving method of super-resolution model for SAR images

2014 ◽  
Author(s):  
X.D. Luan ◽  
W.W. Wang ◽  
Y.X. Xie ◽  
C. Li
Keyword(s):  
Super Resolution ◽  
Sar Images ◽  
Resolution Model

An image Denoising Method Based On Multi Resulation Bilateral Filter

2018 ◽  
Vol 6 (12) ◽  
pp. 448-452
Author(s):  
Md Shaiful Islam Babu ◽  
Kh Shaikh Ahmed ◽  
Md Samrat Ali Abu Kawser ◽  
Ajkia Zaman Juthi
Keyword(s):  
Image Denoising ◽  
Bilateral Filter ◽  
Denoising Method
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