The goal of ratio enhancement for hyperspectral (HS) image pansharpening is to obtain an enhancement ratio between a simulated low-resolution panchromatic (Pan) image and an original high-resolution Pan image. However, the simulated low-resolution Pan image often suffers from gray-level distortion. To solve these problems, the original HS bands are synthesized to a smaller number of reduced HS bands, then the pixels of Pan and HS images are divided into different groups according to the linearity between the Pan band and the reduced bands. For each pixel group, a nonnegative least-squares algorithm is utilized to calculate the weights of reduced HS bands, so that the simulated Pan image is obtained by weighted summation of reduced HS bands. Finally, the HS image is sharpened by a ratio enhancement. The experiments demonstrated that the proposed method had a good performance on fusion quality.
Change detection is of high practical value to hazard assessment, crop growth monitoring, and urban sprawl detection. A synthetic aperture radar (SAR) image is the ideal information source for performing change detection since it is independent of atmospheric and sunlight conditions. Existing SAR image change detection methods usually generate a difference image (DI) first and use clustering methods to classify the pixels of DI into changed class and unchanged class. Some useful information may get lost in the DI generation process. This paper proposed an SAR image change detection method based on neighborhood-based ratio (NR) and extreme learning machine (ELM). NR operator is utilized for obtaining some interested pixels that have high probability of being changed or unchanged. Then, image patches centered at these pixels are generated, and ELM is employed to train a model by using these patches. Finally, pixels in both original SAR images are classified by the pretrained ELM model. The preclassification result and the ELM classification result are combined to form the final change map. The experimental results obtained on three real SAR image datasets and one simulated dataset show that the proposed method is robust to speckle noise and is effective to detect change information among multitemporal SAR images.
The anisotropic scale space (ASS) is often used to enhance the performance of a scale-invariant feature transform (SIFT) algorithm in the registration of synthetic aperture radar (SAR) images. The existing ASS-based methods usually suffer from unstable keypoints and false matches, since the anisotropic diffusion filtering has limitations in reducing the speckle noise from SAR images while building the ASS image representation. We proposed a speckle reducing SIFT match method to obtain stable keypoints and acquire precise matches for the SAR image registration. First, the keypoints are detected in a speckle reducing anisotropic scale space constructed by the speckle reducing anisotropic diffusion, so that speckle noise is greatly reduced and prominent structures of the images are preserved, consequently the stable keypoints can be derived. Next, the probabilistic relaxation labeling approach is employed to establish the matches of the keypoints then the correct match rate of the keypoints is significantly increased. Experiments conducted on simulated speckled images and real SAR images demonstrate the effectiveness of the proposed method.
Existing segmentation methods require manual interventions to optimally extract objects from cluttered background, so that they can hardly work well in automated surveillance systems. In order to automatically extract aircrafts from very high-resolution images, we proposed a segmentation method that combines bottom-up and top-down cues. Three essential principles from local contrast, global contrast, and center bias are involved to compute bottom-up cue. In addition, top-down cue is computed by incorporating aircraft shape priors, and it is achieved by training a classifier from a rich set of visual features. Iterative operations and adaptive fitting are designed to get refined results. Experimental results demonstrated that the proposed method can provide significant improvements on the segmentation accuracy.
Scale-invariance feature transform (SIFT)–based algorithms often suffer from false matches of keypoints while being utilized to register multispectral remote sensing images. It is even worse for the registration of large water areas due to the lack of keypoints and high similarity of textures in such regions. To tackle these problems, a robust and fast SIFT match algorithm for multispectral image registration based on keypoint classification is proposed. This algorithm establishes match candidates through classifying the keypoints based on their scale spaces, where the keypoint matching is restricted on the same scale group. To obtain an accurate scale group, the ground sampling distance of images to be registered is normalized, while the initial scale ratio is estimated, relying upon the seed matches. Furthermore, for refining the match candidates and improving the matching robustness on water areas of the image, the keypoints are classified into keypoints of the land region (L-KPs) and keypoints of the water area (W-KPs). The L-KPs of the match candidates with sparse region on the two-dimensional representation of the location offset are excluded by the iterative bivariate histogram, and then the average location offset of the correct land matches is employed to exclude the outliers of the candidates associated with the W-KPs. Meanwhile, to reduce the computational cost, the images to be registered are divided into corresponding overlapped blocks, providing parallel computing and local rectification. Experiments were conducted on large-size remote sensing images at different resolutions and the results demonstrate the effectiveness of the proposed approach.
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