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In hyperspectral imagery, there exist homogeneous regions where neighboring pixels tend to belong to the same class with high probability. However, even though neighboring pixels are from the same material, their spectral characteristics may be different due to various factors, such as internal instrument noise or atmospheric scattering, which results in misclassification. In this work, the proposed...
In this paper, we propose a joint sparse and collaborative representation-based algorithm for target detection in hyperspectral imagery. The proposed target detection is achieved by the representation of the test samples using a target library and a background library. The sparse representation of given target samples is solved by an ℓ1-norm minimization of the representation weight vector, and the...
We propose a novel collaborative representation based k-nearest neighbors algorithm for hyperspectral image classification. The proposed method is based on a collaborative representation computed by an ℓ2-norm minimization with a Tikhonov regularization matrix. More specific, the testing sample is represented as a linear combination of all the training samples, and the weights for representation are...
Detection of buried radioactive objects faces challenges such as low energy counts and strong background clutters due to the burial of the targets. Classical detection methods such as the constrained energy minimization (CEM) and the RX method, when applied separately, may not be able to yield satisfactory results. In this paper, we propose to combine detection results from individual detectors through...
A method of unsupervised nearest regularized subspace is proposed for anomaly detection in hyperspectral imagery. Based on a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination, for which the weight vector is calculated by distance-weighted Tikhonov regularization. Proposed detector returns the similarity measurement between the testing...
Sparse unmixing has been proposed for hyperspectral image analysis. It has been shown that improved performance can be achieved when endmembers from a spectral library are used. However, when endmembers from image data have to be employed for unmixing, such a sparse-constrained approach may be problematic due to the fact that endmembers are generally highly coherent, thereby producing unstable sparse...
In this paper, a new method for image magnification is presented. The image reduction is seen as a result of image multiplying with a compressed matrix, and the magnification is stated as an inverse problem of reduction. We exploit the reconstruction idea of Compressed Sensing and propose a norm minimization model to solve the inverse problem. The norm reflects the image's natural property - compressive...
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