Abstract:Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused i… Show more
“…The spatial domain algorithm includes many fusion rules such as gray-scale weighted average method [ 12 ], contrast modulation method [ 13 ], and Principal Component Analysis(PCA) [ 7 ], etc. Transform domain algorithms include Wavelet Transform(WT) method [ 14 , 15 ], pyramid decomposition fusion algorithm [ 16 ], Curvelet Transform(CVT) [ 17 ], Non-Subsampled Contourslet Transform(NSCT) [ 18 ], Non-Subsampled Shearlet Transform(NSST) [ 19 ], etc. The algorithm based on transform domain is the current mainstream fusion algorithm of infrared image and visible image [ 8 ].…”
Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high.
“…The spatial domain algorithm includes many fusion rules such as gray-scale weighted average method [ 12 ], contrast modulation method [ 13 ], and Principal Component Analysis(PCA) [ 7 ], etc. Transform domain algorithms include Wavelet Transform(WT) method [ 14 , 15 ], pyramid decomposition fusion algorithm [ 16 ], Curvelet Transform(CVT) [ 17 ], Non-Subsampled Contourslet Transform(NSCT) [ 18 ], Non-Subsampled Shearlet Transform(NSST) [ 19 ], etc. The algorithm based on transform domain is the current mainstream fusion algorithm of infrared image and visible image [ 8 ].…”
Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high.
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