scholarly journals Multiple Kernel Graph Cut for SAR Image Change Detection

2021 ◽  
Vol 13 (4) ◽  
pp. 725
Author(s):  
Lu Jia ◽  
Tiantian Zhang ◽  
Jing Fang ◽  
Feibiao Dong
Keyword(s):  
Change Detection ◽  
Energy Function ◽  
Multiple Kernel Learning ◽  
Graph Cut ◽  
Sar Image ◽  
Complementary Information ◽  
Data Set ◽  
Kernel Weights ◽  
Multiple Kernel ◽  
Image Change Detection

Complementary information between two difference images (DI’s) has great contribution to improve change detection performances. Based on the effectiveness and flexibility of the multiple kernel learning (MKL) in information fusion, we develop a multiple kernel graph cut (MKGC) algorithm for synthetic aperture radar (SAR) image change detection. An energy function containing a weighted summation kernel is proposed for fusing the complementary information between the subtraction image and the ratio image. By iteratively minimizing the energy function, the kernel weights, region parameters and region labels are estimated automatically and optimally. Besides of it avoids modeling, MKGC also has a complete description of the changed areas and the strong noise immunity. Experiments on real GaoFen-3 SAR data set demonstrate the effectiveness of the MKGC algorithm, and illustrate that it is a good candidate for SAR image change detection.

scholarly journals A Multiple Kernel Learning Model Based on p-Norm

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Jinshan Qi ◽  
Xun Liang ◽  
Rui Xu
Keyword(s):  
Multiple Kernel Learning ◽  
Research Society ◽  
Kernel Functions ◽  
Support Vector ◽  
Kernel Weights ◽  
Multiple Kernel ◽  
Vector Machines ◽  
Norm Constraint ◽  
Special Case ◽  
Elastic Constraint

By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ L1-norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the Lp-norm constraint on the kernel weights keeps all information in the base kernels. Nonetheless, the solution of Lp-norm constraint MKL is nonsparse and sensitive to the noise. Recently, some scholars presented an efficient sparse generalized MKL (L1- and L2-norms based GMKL) method, in which L1  L2 established an elastic constraint on the kernel weights. In this paper, we further extend the GMKL to a more generalized MKL method based on the p-norm, by joining L1- and Lp-norms. Consequently, the L1- and L2-norms based GMKL is a special case in our method when p=2. Experiments demonstrated that our L1- and Lp-norms based MKL offers a higher accuracy than the L1- and L2-norms based GMKL in the classification, while keeping the properties of the L1- and L2-norms based on GMKL.

scholarly journals SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain

2018 ◽  
Vol 51 (1) ◽  
pp. 785-794 ◽  
Author(s):  
Zhou Wenyan ◽  
Jia Zhenhong ◽  
Yinfeng Yu ◽  
Jie Yang ◽  
Nilola Kasabov
Keyword(s):  
Change Detection ◽  
Image Fusion ◽  
Adaptive Threshold ◽  
Equal Weight ◽  
Sar Image ◽  
Image Change Detection

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.

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