Locality-preserving nonnegative matrix factorization for hyperspectral image classification

Li, Wei; Prasad, Saurabh; Fowler, James E.; Cui, Minshan

doi:10.1109/igarss.2012.6351273

Cited by 6 publications

(5 citation statements)

References 8 publications

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“…The solution to problem (12) can be transferred to equivalently solve the problem (13) with ADMM. Considering the fact that the object function in (13) is not convex with respect to all variables simultaneously, but it is a convex problem regarding the separate variable when other variables are fixed, therefore we successively minimize L μ (13)…”

Section: Discussionmentioning

confidence: 99%

“…The solution to problem (12) can be transferred to equivalently solve the problem (13) with ADMM. Considering the fact that the object function in Eq.…”

Section: Appendix a Solution To Autorulementioning

confidence: 99%

“…Over the past decade, SL techniques have been widely used in remote sensing data processing and analysis [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], particularly hyperspectral dimensionality reduction (HDR) [12]. Li et al [13] carried out the HDR and classification by locally preserving neighborhood relations. In [14], spectral-spatial noise estimation can largely enhance the performance of dimensionality reduction, and the proposed method not only can extract high-quality features but also can well deal with nonlinear problems in hyperspectral image classification.…”

Section: Introductionmentioning

confidence: 99%

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Joint and Progressive Subspace Analysis (JPSA) With Spatial–Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction

Hong

Yokoya

Chanussot

et al. 2021

IEEE Trans. Cybern.

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This is the pre-acceptance version, to read the final version please go to IEEE Transactions on Cybernetics on IEEE Xplore. Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination). To overcome these shortcomings, a novel linearized subspace analysis technique with spatial-spectral manifold alignment is developed for a semi-supervised hyperspectral dimensionality reduction (HDR), called joint and progressive subspace analysis (JPSA). The JPSA learns a high-level, semantically meaningful, joint spatialspectral feature representation from hyperspectral data by 1) jointly learning latent subspaces and a linear classifier to find an effective projection direction favorable for classification; 2) progressively searching several intermediate states of subspaces to approach an optimal mapping from the original space to a This paper is an extended version in algorithm of [1] published in ECCV2018.

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Section: Discussionmentioning

confidence: 99%

“…The solution to problem (12) can be transferred to equivalently solve the problem (13) with ADMM. Considering the fact that the object function in Eq.…”

Section: Appendix a Solution To Autorulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint and Progressive Subspace Analysis (JPSA) With Spatial–Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction

Hong

Yokoya

Chanussot

et al. 2021

IEEE Trans. Cybern.

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“…LPNMF combines the advantages of LPP and the nonnegative matrix factorization (NMF). The NMF decomposes the data into two non-negative matrices and extracts features for HSI classification, unmixing of HSI and other applications (Li, Prasad, Fowler, & Cui, 2012). LFDA combines the properties of LDA and LPP, exploiting the advantages of each and preserving the neighbourhood relationships within the embedding by employing an "affinity" matrix.…”

Section: Introductionmentioning

confidence: 99%

Decision fusion for hyperspectral image classification based on multiple features and locality-preserving analysis

Bai

Nian

2017

European Journal of Remote Sensing

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A novel fusion-classification system is proposed for hyperspectral image classification. Firstly, spectral derivatives are used to capture salient spectral features for different land-cover classes and a Gabor filter is applied to extract useful spatial features at neighbouring locations. Then, two locality-preserving dimensionality reduction methods are employed to reduce the dimensionality of data and preserve the local structure of neighbouring samples in the original image, derivative-feature and Gabor-feature domains. Finally, the classification results from Gaussian-mixture-model classifiers are fused by a decision-fusion approach. We have compared the proposed system to several traditional and state-of-the-art methods on two benchmark classification data sets. In both cases, our system achieved improved accuracy than the current best performing methods. Especially in the case of classification for the Indian Pines data set, the proposed DG-locality-preserving nonnegative matrix factorization (LPNMF) has 7% higher accuracy than support vector machine-Markov random field and LPNMF-G-MRF, and also has about 4% higher accuracy than Gabor-LPNMF. The proposed method has practical relevance as it shows potential to create smooth classification maps even for complex, spatially heterogeneous land-cover classes.

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“…Among many other approaches such as morphological filtering [22], maximum noise fraction (MNF) [23] and Nonnegative Matrix Factorization (NMF) [24], Markov random fields (MRF) is particularly useful as it helps to extract the spatial dependency in a Bayesian method for HSI classification. In [6], a novel MRF-based MLR classifier is proposed.…”

Section: Introductionmentioning

confidence: 99%

Spatial‐spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling

Qing

Ruan

et al. 2019

IET Image Processing

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This version is available at https://strathprints.strath.ac.uk/65510/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Strathprints administrator: strathprints@strath.ac.ukThe Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the management and persistent access to Strathclyde's intellectual output. Abstract: For spatial-spectral classification of hyperspectral images (HSI), a deep learning framework is proposed in this paper, which consists of convolutional neural networks (CNN) and Markov random fields (MRF). Firstly, a CNN model to learn the deep spectral feature from the HSI is built and the class posterior probability distribution is estimated. The CNN with a dropout layer can relieve the overfitting in classification. The CNN is utilized as a pixel-classifier, so it only works in the spectral domain. Then, the spatial information will be encoded by MRF-based multilevel logistic (MLL) prior for regularizing the classification. To derive the correlation of both spectral and spatial features for improving algorithm performance, the marginal probability distribution in HSI is learned using MRF-based loopy belief propagation (LBP). In comparison with several state-of-the-art approaches for data classification on 3 publicly available HSI datasets, experimental results have demonstrated the superior performance of the proposed methodology. Keywords Hyperspectral image (HSI); spatial-spectral classification; convolutional neural networks (CNN); Markov random fields (MRF); loopy belief propagation (LBP). IntroductionHyperspectral remote sensing, a technology of acquiring remote sensing image in high-resolution spectrum, is capable of simultaneously collecting spectral and spatial information for earth observation, especially land cover analysis [1]. As an emerging field, hyperspectral remote sensing has been introduced in a wide range of scenes increasingly. Even for hyperspectral image (HSI)-based classification and target detection, the technology has been successfully applied in aerospace, agriculture, forestry, mineral, atmospheric sciences, military and so on [2] [3]. Apart from these conventional applicati...

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Locality-preserving nonnegative matrix factorization for hyperspectral image classification

Cited by 6 publications

References 8 publications

Joint and Progressive Subspace Analysis (JPSA) With Spatial–Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction

Joint and Progressive Subspace Analysis (JPSA) With Spatial–Spectral Manifold Alignment for Semisupervised Hyperspectral Dimensionality Reduction

Decision fusion for hyperspectral image classification based on multiple features and locality-preserving analysis

Spatial‐spectral classification of hyperspectral images: a deep learning framework with Markov Random fields based modelling

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