Learning Discriminative Sparse Representations for Modeling, Source Separation, and Mapping of Hyperspectral Imagery

Castrodad, Alexey; Xing, Zhengming; Greer, John B.; Bosch, Edward H.; Carin, Lawrence; Sapiro, Guillermo

doi:10.1109/tgrs.2011.2163822

Cited by 114 publications

(64 citation statements)

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“…From an end user's viewpoint, it depends on the scene and whether one can preselect a reliable library for that scene specifically. Moreover, there are concurrent studies that consider learning the dictionary from the data, thereby circumventing these issues [51], [60], [61]. Dictionary learning is an active research topic.…”

Section: Further Discussionmentioning

confidence: 99%

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Bioucas-Dias

Chan

et al. 2014

IEEE Signal Process. Mag.

421

322

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Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most prominent research topics in signal processing (SP) for hyperspectral remote sensing [1], [2]. Blind HU aims at identifying materials present in a captured scene, as well as their compositions, by using high spectral resolution of hyperspectral images. It is a blind source separation (BSS) problem from a SP viewpoint. Research on this topic started in the 1990s in geoscience and remote sensing [3]- [7], enabled by technological advances in hyperspectral sensing at the time. In recent years, blind HU has attracted much interest from other fields such as SP, machine learning, and optimization, and the subsequent cross-disciplinary research activities have made blind HU a vibrant topic. The resulting impact is not just on remote sensing-blind HU has provided a unique problem scenario that inspired researchers from different fields to devise novel blind SP methods. In fact, one may say that blind HU has established a new branch of BSS approaches not seen in classical BSS studies. In particular, the convex geometry concepts-discovered by early remote sensing researchers through empirical observations [3]- [7] and refined by later research-are elegant and very different from statistical independence-based BSS approaches established in

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Section: Further Discussionmentioning

confidence: 99%

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Bioucas-Dias

Chan

et al. 2014

IEEE Signal Process. Mag.

421

322

View full text Add to dashboard Cite

show abstract

“…In addition, max pooling [4][19] is used for feature pooling, which experimental demonstrates better performance for the sparse coding based image representation. Moreover, other sparse coding based image representations [5][20] [21] have also been proposed and demonstrate good performance for basic-level image classification. B.…”

Section: Related Workmentioning

confidence: 99%

Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization

Gao

Tsang

2014

IEEE Trans. on Image Process.

133

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Abstract-This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. Moreover, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five datasets show that our method can outperform the state-of-the-art finegrained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

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“…Among many surveys about hyperspectral imagery (HSI) analysis, land cover accurate classification is an important research topic. Supervised spectral classifiers are popular in the early research, including multinomial logistic regression [5], support vector machines (SVMs) [6][7][8] and sparse representation classifier [9].…”

Section: Introductionmentioning

confidence: 99%

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

Pan

Shi

et al. 2017

Remote Sensing

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Abstract:Integrating spectral and spatial information is proved effective in improving the accuracy of hyperspectral imagery classification. In recent studies, two kinds of approaches are widely investigated: (1) developing a multiple feature fusion (MFF) strategy; and (2) designing a powerful spectral-spatial feature extraction (FE) algorithm. In this paper, we combine the advantages of MFF and FE, and propose an ensemble based feature representation method for hyperspectral imagery classification, which aims at generating a hierarchical feature representation for the original hyperspectral data. The proposed method is composed of three cascaded layers: firstly, multiple features, including local, global and spectral, are extracted from the hyperspectral data. Next, a new hashing based feature representation method is proposed and conducted on the features obtained in the first layer. Finally, a simple but efficient extreme learning machine classifier is employed to get the classification results. To some extent, the proposed method is a combination of MFF and FE: instead of feature fusion or single feature extraction, we use an ensemble strategy to provide a hierarchical feature representation for the hyperspectral data. In the experiments, we select two popular and one challenging hyperspectral data sets for evaluation, and six recently proposed methods are compared. The proposed method achieves respectively 89.55%, 99.36% and 77.90% overall accuracies in the three data sets with 20 training samples per class. The results prove that the performance of the proposed method is superior to some MFF and FE based ones.

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Learning Discriminative Sparse Representations for Modeling, Source Separation, and Mapping of Hyperspectral Imagery

Cited by 114 publications

References 50 publications

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

A Signal Processing Perspective on Hyperspectral Unmixing: Insights from Remote Sensing

Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization

Hashing Based Hierarchical Feature Representation for Hyperspectral Imagery Classification

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