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Sparsity-preserving graph construction is investigated for the dimensionality reduction of hyperspectral imagery. In particular, a sparse graph-based discriminant analysis is proposed when labeled samples are available. By forcing the projection to be along the direction where a sample is clustered with within-class samples that best represented it, the(More)
Random projections have recently been proposed to enable dimensionality reduction in resource-constrained sensor devices such that the computational burden is shifted to the receiver side of the system in the form of a reconstruction process. While a number compressed-sensing algorithms can provide such reconstruction, the principal-component based(More)
In lossy compression such as PCA+JPEG2000 for hyperspectral imagery, the bitrate is usually not fixed, resulting in various rate-distortion performance. In this paper, we propose an operational approach to determine the approximately optimal bitrate to be used to preserve both the majority of the information in the dataset as well as the anomalous pixels.(More)
A process for the use of multihypothesis prediction in the reconstruction of images is proposed for use in both compressed-sensing reconstruction as well as single-image super-resolution. Specifically, for compressed-sensing reconstruction of a single still image , multiple predictions for an image block are drawn from spatially surrounding blocks within an(More)
In this paper, we investigate the performance of a sparsity-preserving graph embedding based approach, called [I graph, in hyperspectral image dimensionality reduction (DR), and propose noise-adjusted sparsity­ preserving(NASP) based DR when training samples are unavailable. In conjunction with the state-of-the-art hyperspectral image classifier, support(More)
Hyperspectral image dimensionality reduction with graph-based approaches is considered. With available labeled samples, a graph can be formed with these samples by constructing an affinity matrix through their sparse or collaborative representations. In addition, sparse or collaborative representation can be done using within-class samples, resulting in(More)
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