Nearest Regularized Subspace for Hyperspectral Classification

Li, Wei; Tramel, Eric W.; Prasad, Saurabh; Fowler, James E.

doi:10.1109/tgrs.2013.2241773

Cited by 213 publications

(80 citation statements)

References 31 publications

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“…In hyperspectral image analysis, the wealthy spectral information at the cost of high spectral dimensionality can better classify the materials in an observed area. However, high dimensionality leads to the curse of the dimensionality problem, which causes classification performance to deteriorate, especially when the number of available labeled training samples is limited [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

Feng

et al. 2017

Remote Sensing

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Abstract:Recently, graph embedding has drawn great attention for dimensionality reduction in hyperspectral imagery. For example, locality preserving projection (LPP) utilizes typical Euclidean distance in a heat kernel to create an affinity matrix and projects the high-dimensional data into a lower-dimensional space. However, the Euclidean distance is not sufficiently correlated with intrinsic spectral variation of a material, which may result in inappropriate graph representation. In this work, a graph-based discriminant analysis with spectral similarity (denoted as GDA-SS) measurement is proposed, which fully considers curves changing description among spectral bands. Experimental results based on real hyperspectral images demonstrate that the proposed method is superior to traditional methods, such as supervised LPP, and the state-of-the-art sparse graph-based discriminant analysis (SGDA).

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Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

Feng

et al. 2017

Remote Sensing

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“…The proposed methods, NFLE [20,21], KNFLE, FNFLE [26], and FKNFLE, were compared with two state-of-the-art algorithms, i.e., nearest regularized subspace (NRS) [25] and NRS-LFDA [25]. The parameter configurations for both algorithms NRS [29] and NRS-LFDA were as seen in [25].…”

Section: Classification Resultsmentioning

confidence: 99%

A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

et al. 2015

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Abstract:In this paper, a general nearest feature line (NFL) embedding (NFLE) transformation called fuzzy-kernel NFLE (FKNFLE) is proposed for hyperspectral image (HSI) classification in which kernelization and fuzzification are simultaneously considered. Though NFLE has successfully demonstrated its discriminative capability, the non-linear manifold structure cannot be structured more efficiently by linear scatters using the linear NFLE method. According to the proposed scheme, samples were projected into a kernel space and assigned larger weights based on that of their neighbors. The within-class and between-class scatters were calculated using the fuzzy weights, and the best transformation was obtained by maximizing the Fisher criterion in the kernel space. In that way, the kernelized manifold learning preserved the local manifold structure in a Hilbert space as well as the locality of the manifold structure in the reduced low-dimensional space. The proposed method was compared with various state-of-the-art methods to evaluate the performance using three benchmark data sets. Based on the experimental results: the proposed FKNFLE outperformed the other, more conventional methods.

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“…Using the technique in Section 3, 15 bands are generated with multiplication, and another 15 bands are generated with division. There are, in total, 16 different classes from the original ground truth; however, we select eight classes from the original dataset from a statistic viewpoint [5]. The eight classes we used in the experiments are Corn-no-till, Corn-min-till, Grass-pasture, Hay-windowed, Soybean-no-till, Soybean-min-till, Soybean-clean, and woods.…”

Section: Data Description and Experimental Setupmentioning

confidence: 99%

“…Instead, in these methods, a testing pixel is classified based on representation residual using labeled samples. The nearest regularized subspace (NRS) [5] is an improved version of CRC, where samples similar to the testing pixels are allowed to have high weights in the representation. Other variants of SRC or CRC have been proposed for hyperspectral imagery.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers

et al. 2017

Remote Sensing

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This paper investigates representation-based classification for multispectral imagery. Due to small spectral dimension, the performance of classification may be limited, and, in general, it is difficult to discriminate different classes with multispectral imagery. Nonlinear band generation method with explicit functions is proposed to use which can provide additional spectral information for multispectral image classification. Specifically, we propose the simple band ratio function, which can yield better performance than the nonlinear kernel method with implicit mapping function. Two representation-based classifiers-i.e., sparse representation classifier (SRC) and nearest regularized subspace (NRS) method-are evaluated on the nonlinearly generated datasets. Experimental results demonstrate that this dimensionality-expansion approach can outperform the traditional kernel method in terms of high classification accuracy and low computational cost when classifying multispectral imagery.

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Nearest Regularized Subspace for Hyperspectral Classification

Cited by 213 publications

References 31 publications

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

Dimensionality Reduction of Hyperspectral Image with Graph-Based Discriminant Analysis Considering Spectral Similarity

A Dimension Reduction Framework for HSI Classification Using Fuzzy and Kernel NFLE Transformation

Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers

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