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Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded,(More)
Band selection is often applied to reduce the dimensionality of hyperspectral imagery. When the desired object information is known, it can be achieved by finding the bands that contain the most object information. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose a new(More)
in an ordered Banach space E with positive cone K, where M >0 is a constant, f : [0, 1] × K × K ® K is continuous, S : C([0, 1], K) ® C([0, 1], K) is a Fredholm integral operator with positive kernel. Under more general order conditions and measure of noncompactness conditions on the nonlinear term f, criteria on existence of positive solutions are(More)
The high dimensionality of hyperspectral imagery challenges image processing and analysis. Band selection is a common technique for dimensionality reduction. When the desired object information is unknown, an unsupervised band selection approach is employed to select the most distinctive and informative bands. Although band selection can significantly(More)
Principal component analysis (PCA) is widely used for spectral decorrelation in the JPEG2000 compression of hyperspectral imagery. However, due to the data-dependent nature of principal components, the principal component transform matrix is stored in the JPEG2000 bitstream, constituting an overhead that is often negligible if the spatial size of the image(More)
Band clustering is applied to dimensionality reduction of hyperspectral imagery. Different from unsupervised clustering using all the pixels or supervised clustering requiring labeled pixels, the proposed semisupervised band clustering needs class spectral signatures only. After clustering, a cluster selection step is applied to select clusters to be used(More)
A decision fusion approach is developed to combine the results from supervised and unsupervised classifiers. The final output takes advantage of the power of a support-vector-machine-based supervised classification in class separation and the capability of an unsupervised classifier, such as K -means clustering, in reducing trivial spectral variation impact(More)
In this paper, we propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small(More)
We propose a particle swarm optimization (PSO)-based dimensionality reduction approach to improve support vector machine (SVM)-based classification for high-resolution hyperspectral imagery. After a searching criterion function is well designed, PSO can find a global optimal solution much more efficiently, compared to other frequently used searching(More)
Optical flow and its extensions have been widely used in motion detection and computer vision. In this paper, we apply principle component analysis (PCA) to analyze optical flows for better motion detection performance. The joint optical flow and PCA approach can efficiently detect moving objects and suppress small turbulence. It is effective in both static(More)