Semisupervised Band Clustering for Dimensionality Reduction of Hyperspectral Imagery

Su, Hongjun; Yang, He; Du, Qian; Sheng, Yehua

doi:10.1109/lgrs.2011.2158185

Cited by 93 publications

(36 citation statements)

References 12 publications

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“…In KSRC and KCRC, the radial basis function (RBF) is chosen as the kernel function. According to [12], the parameter γ of the kernel function is set as the median value of 1/(||x i − x|| 2 2 ), i = 1, 2, . .…”

Section: Parameter Tuningmentioning

confidence: 99%

“…The high spectral resolution of hyperspectral imagery provides major advantages for classification and detection. However, due to the high dimensionality, its vast data volume can cause issues in data transmission, storage, and analysis [1,2]. Although multispectral imagery has low spectral resolution and it may be difficult to distinguish materials with similar spectral signatures, its high spatial resolution and wide coverage make it still popular in practical applications.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Parameter Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers

et al. 2017

Remote Sensing

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“…As shown in Table 3, the combination of MLR, KNN and ELM is the optimal one. For two methods to be compared, let f 11 denote the number of samples that both methods can correctly classify, f 22 the number of samples that both cannot, f 12 the number of samples misclassified by Method 1, but not Method 2, and f 21 the number of samples misclassified by Method 2, but not Method 1 [42]. Then, the decision criterion of McNemar's test statistic is: For a 5% level of significance, the corresponding |z| value is 1.96; a |z| value greater than this quantity means that two methods have significant performance discrepancy.…”

Section: Parameter Settingmentioning

confidence: 99%

A Novel Tri-Training Technique for Semi-Supervised Classification of Hyperspectral Images Based on Diversity Measurement

Tan

Zhu

et al. 2016

Remote Sensing

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This paper introduces a novel semi-supervised tri-training classification algorithm based on diversity measurement for hyperspectral imagery. In this algorithm, three measures of diversity, i.e., double-fault measure, disagreement metric and correlation coefficient, are applied to select the optimal classifier combination from different classifiers, e.g., support vector machine (SVM), multinomial logistic regression (MLR), extreme learning machine (ELM) and k-nearest neighbor (KNN). Then, unlabeled samples are selected using an active learning (AL) method, and consistent results of any other two classifiers combined with a spatial neighborhood information extraction strategy are employed to predict their labels. Moreover, a multi-scale homogeneity (MSH) method is utilized to refine the classification result with the highest accuracy in the classifier combination, generating the final classification result. Experiments on three real hyperspectral data indicate that the proposed approach can effectively improve classification performance.

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“…977 extraction (DAFE), decision boundary feature extraction (DBFE), and non-parametric weighted feature extraction (NWFE), among many others [4][5][6][7]. Recently, it was shown by Lee and Seung that positivity or non-negativity of a linear expansion is a very powerful constraint that also seems to yield sparse representations [8,9].…”

Section: Rvm Classification Of Hyperspectral Images Based On Wavelet mentioning

confidence: 99%

RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

Bai

Hui

et al. 2015

TELKOMNIKA

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Semisupervised Band Clustering for Dimensionality Reduction of Hyperspectral Imagery

Cited by 93 publications

References 12 publications

Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers

Nonlinear Classification of Multispectral Imagery Using Representation-Based Classifiers

A Novel Tri-Training Technique for Semi-Supervised Classification of Hyperspectral Images Based on Diversity Measurement

RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

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