Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Wei, Yantao; Zhou, Yicong; Li, Hong

doi:10.3390/rs9030203

Cited by 26 publications

(10 citation statements)

References 73 publications

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“…Hyperspectral images have hundreds of spectral bands, in contrast with RGB images which have only three spectral bands. The multiple spectral bands and high resolution make hyperspectral imagery essential in remote sensing, target analysis, classification and identification [10,15,21,24,36,38,40]. Two publicly available datasets are used to evaluate the effectiveness of TGCA and GCA for supervised classification.…”

Section: Classificationmentioning

confidence: 99%

Generalized Visual Information Analysis Via Tensorial Algebra

Liao

Maybank

2020

J Math Imaging Vis

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High-order data are modeled using matrices whose entries are numerical arrays of a fixed size. These arrays, called t-scalars, form a commutative ring under the convolution product. Matrices with elements in the ring of t-scalars are referred to as t-matrices. The t-matrices can be scaled, added and multiplied in the usual way. There are t-matrix generalizations of positive matrices, orthogonal matrices and Hermitian symmetric matrices. With the t-matrix model, it is possible to generalize many well-known matrix algorithms. In particular, the t-matrices are used to generalize the singular value decomposition (SVD), high-order SVD (HOSVD), principal component analysis (PCA), two-dimensional PCA (2DPCA) and Grassmannian component analysis (GCA). The generalized t-matrix algorithms, namely TSVD, THOSVD, TPCA, T2DPCA and TGCA, are applied to low-rank approximation, reconstruction and supervised classification of images. Experiments show that the t-matrix algorithms compare favorably with standard matrix algorithms.

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Section: Classificationmentioning

confidence: 99%

Generalized Visual Information Analysis Via Tensorial Algebra

Liao

Maybank

2020

J Math Imaging Vis

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“…Extensive experiments are conducted to make the comprehensive comparisons with other state-of-the-art methods. Firstly, we make the comparison of the recently proposed methods, including multiview disagreement-intersection (MV3D-DisInt) based method [34], multiview disagreement-singularity (MV3D-DisSin) based method [34], Gabor-breaking ties (Gabor-BT) [43] and PCA-Gabor scheme [16]. To make more specifical comparison, we compare the 3D-Gabor-IEUE with other view generation and query selection methods as well.…”

Section: Methodsmentioning

confidence: 99%

“…To alleviate the above-mentioned problem, a lot of methods have been proposed in literature. For instance, band selection is studied to reduce the redundancy between contiguous bands [15] and spectral-spatial feature extraction takes advantages of the more distinguishable characteristics [16,17]. However, among those methods, sufficient labeled samples/pixels are crucial to get the reliable classification results [18].…”

Section: Introductionmentioning

confidence: 99%

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Hu¹,

He²,

Li³

et al. 2018

Remote Sensing

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Active learning (AL) has been shown to be very effective in hyperspectral image (HSI) classification. It significantly improves the performance by selecting a small quantity of the most informative training samples to reduce the complexity of classification. Multiview AL (MVAL) can make the comprehensive analysis of both object characterization and sampling selection in AL by using various features of multiple views. However, the original MVAL cannot effectively exploit the spectral-spatial information by respecting the three-dimensional (3D) nature of the HSI and the query selection strategy in the MVAL is only based on the disagreement of multiple views. In this paper, we propose a 3D-Gabor inspired MVAL method for spectral-spatial HSI classification, which consists of two main steps. First, in the view generation step, we adopt a 3D-Gabor filter to generate multiple cubes with limited bands and utilize the feature assessment strategies to select cubes for constructing views. Second, in the sampling selection step, a novel method is proposed by using both internal and external uncertainty estimation (IEUE) of views. Specifically, we use the distributions of posterior probability to learn the "internal uncertainty" of each independent view, and adopt the inconsistencies between views to estimate the "external uncertainty". Classification accuracies of the proposed method for the four benchmark HSI datasets can be as high as 99.57%, 99.93%, 99.02%, 98.82%, respectively, demonstrating the improved performance as compared with other state-of-the-art methods.

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“…(1) Our method integrates a manifold learning algorithm with BLS in a multilayer neural network model, thus providing enhanced feature representation capacity to BLS (2) A novel implementation of spectral-spatial response (SSR) [37] consisting of Gabor filter and adaptive weighted filter (AWF) is developed to extract deep features of HSIs without a deep learning scheme (3) With comparative experiments conducted on 3 standard HSIs datasets, we show the proposed approach's advantage in classification accuracy over the state-of-the-art methods e rest of this paper is organized as follows. Section 2 gives a brief overview of BLS.…”

Section: Introductionmentioning

confidence: 99%

Broad Learning System with Locality Sensitive Discriminant Analysis for Hyperspectral Image Classification

Yao

Wei

Tian

2020

Mathematical Problems in Engineering

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In this paper, we propose a new method for hyperspectral images (HSI) classification, aiming to take advantage of both manifold learning-based feature extraction and neural networks by stacking layers applying locality sensitive discriminant analysis (LSDA) to broad learning system (BLS). BLS has been proven to be a successful model for various machine learning tasks due to its high feature representative capacity introduced by numerous randomly mapped features. However, it also produces redundancy, which is indiscriminate and finally lowers its performance and causes heavy computing demand, especially in cases of the input data bearing high dimensionality. In our work, a manifold learning method is integrated into the BLS by inserting two LSDA layers before the input layer and output layer separate, so the spectral-spatial HSI features are fully utilized to acquire the state-of-the-art classification accuracy. The extensive experiments have shown our method’s superiority.

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Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Cited by 26 publications

References 73 publications

Generalized Visual Information Analysis Via Tensorial Algebra

Generalized Visual Information Analysis Via Tensorial Algebra

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Broad Learning System with Locality Sensitive Discriminant Analysis for Hyperspectral Image Classification

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