Sparse Representation for Target Detection in Hyperspectral Imagery

By Yi Chen, Nasser M. Nasrabadi, Trac Duy Tran. IEEE Journal on Selected Topics in Signal Processing, 2011

416 citations recorded by the source corpus.

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Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features (2013, background): “In [38], [39], [40], a hyperspectral pixel is sparsely represented by a linear combination of a few training samples from a structured dictionary.”
A Low-Rank and Sparse Matrix Decomposition-Based Mahalanobis Distance Method for Hyperspectral Anomaly Detection (2016, methodology): “GoDec is a fast approximation algorithm, which replaces the time-consuming SVD/PCA with the bilateral random projections (BRPs) [26] in RAMD.”
Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding (2014, methodology): “For example, Chen et al. (2011) proposed a method for target detection in hyperspectral imagery based on sparse representation, which exploits the sparsity constraint, reconstruction error and the neighboring pixels that have similar spectral attributes.”
Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images (2016, background): “sensing image analysis [9]–[11], [14]–[16], [37], [38] and have shown excellent performances.”
Hyperspectral image classification via kernel sparse representation (2011, methodology): “This section briefly introduces the sparsity-based algorithm for HSI classification, and more details can be found in [26]– [28].”