Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

Hong, Huixiao; Meili, Chen; Duan, Yule

doi:10.3390/rs11091039

Cited by 17 publications

(10 citation statements)

References 48 publications

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“…Then, the nearest neighbor classifier (NN) [55] was used for classification. After that, the classification accuracy of each class (CA), overall classification accuracy (OA), average classification accuracy (AA) and kappa coeffcient (KC) were adopted to evaluate the performance of different DR methods [47,52]. Among them, CA is the classification accuracy on each class of land covers, AA is a measure of the mean value of the classification accuracies of all classes, OA refers to the number of correctly classified instances divided by the total number of test samples, KC is a statistical measurement of consistency between the ground truth map and the final classification map.…”

Section: Methodsmentioning

confidence: 99%

“…According to the spatial distribution consistency of HSI, the pixels are generally distributed in blocks, such as Soil, Water, Building and Woods [51]. Therefore, the neighboring pixels in a spatial window are more likely belonging to the same class and they lie on the same manifold [52]. To utilize the spatial information in HSI, a spatial-domain weighted intramanifold scatter matrix is designed to characterize the similarity relationship for each submanifold and a spatial-domain weighted intermanifold scatter matrix is defined to represent the dissimilarity relationship between different submanifolds.…”

Section: Spatial-domain Multi-manifold Analysis Modelmentioning

confidence: 99%

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Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

et al. 2019

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Hyperspectral images (HSI) possess abundant spectral bands and rich spatial information, which can be utilized to discriminate different types of land cover. However, the high dimensional characteristics of spatial-spectral information commonly cause the Hughes phenomena. Traditional feature learning methods can reduce the dimensionality of HSI data and preserve the useful intrinsic information but they ignore the multi-manifold structure in hyperspectral image. In this paper, a novel dimensionality reduction (DR) method called spatial-spectral multiple manifold discriminant analysis (SSMMDA) was proposed for HSI classification. At first, several subsets are obtained from HSI data according to the prior label information. Then, a spectral-domain intramanifold graph is constructed for each submanifold to preserve the local neighborhood structure, a spatial-domain intramanifold scatter matrix and a spatial-domain intermanifold scatter matrix are constructed for each sub-manifold to characterize the within-manifold compactness and the between-manifold separability, respectively. Finally, a spatial-spectral combined objective function is designed for each submanifold to obtain an optimal projection and the discriminative features on different submanifolds are fused to improve the classification performance of HSI data. SSMMDA can explore spatial-spectral combined information and reveal the intrinsic multi-manifold structure in HSI. Experiments on three public HSI data sets demonstrate that the proposed SSMMDA method can achieve better classification accuracies in comparison with many state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

Section: Spatial-domain Multi-manifold Analysis Modelmentioning

confidence: 99%

Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

et al. 2019

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“…56 Hypergraph learning methods have also been introduced to explore the multiple adjacency relationship in hyperspectral data and discover the complex geometric structure between hyperspectral images. 57 Discriminant hyper-Laplacian projection, 57 semisupervise hypergraph embedding, 58 local pixel NPE, 59 and spatial-spectral regularized sparse hypergraph embedding 57 are some types of graph embedding methods for dimensionality reduction of hyperspectral images.…”

Section: Dimensionality Processingmentioning

confidence: 99%

Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

Peyghambari

Zhang

2021

J. Appl. Rem. Sens.

108

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Hyperspectral imaging has been used in a variety of geological applications since its advent in the 1970s. In the last few decades, different techniques have been developed by geologists to analyze hyperspectral data in order to quantitatively extract geological information from the high-spectral-resolution remote sensing images. We attempt to review and update various steps of the techniques used in geological information extraction, such as lithological and mineralogical mapping, ore exploration, and environmental geology. The steps include atmospheric correction, dimensionality processing, endmember extraction, and image classification. It is identified that per-pixel and subpixel image classifiers can generate accurate alteration mineral maps. Producing geological maps of different surface materials including minerals and rocks is one of the most important geological applications. The hyperspectral images classification methods demonstrate the potential for being used as a main tool in the mining industry and environmental geology. To exemplify the potential, we also include a few case studies of different geological applications.

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“…In recent years, scholars have put forward many DR methods, which can be divided into the following two categories: linear dimensionality reduction (LDR) algorithms and manifold dimensionality reduction (MDR) algorithms [ 6 ]. The former includes principal component analysis (PCA) [ 7 ], linear discriminant analysis (LDA) [ 8 ], and independent component analysis (ICA) [ 9 ], and so on. These methods project images to the low-dimensional space by linear transformation and find the optimal transformation projection.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

Liu

Xia

et al. 2020

Sensors

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Due to the spectral complexity and high dimensionality of hyperspectral images (HSIs), the processing of HSIs is susceptible to the curse of dimensionality. In addition, the classification results of ground truth are not ideal. To overcome the problem of the curse of dimensionality and improve classification accuracy, an improved spatial–spectral weight manifold embedding (ISS-WME) algorithm, which is based on hyperspectral data with their own manifold structure and local neighbors, is proposed in this study. The manifold structure was constructed using the structural weight matrix and the distance weight matrix. The structural weight matrix was composed of within-class and between-class coefficient representation matrices. These matrices were obtained by using the collaborative representation method. Furthermore, the distance weight matrix integrated the spatial and spectral information of HSIs. The ISS-WME algorithm describes the whole structure of the data by the weight matrix constructed by combining the within-class and between-class matrices and the spatial–spectral information of HSIs, and the nearest neighbor samples of the data are retained without changing when embedding to the low-dimensional space. To verify the classification effect of the ISS-WME algorithm, three classical data sets, namely Indian Pines, Pavia University, and Salinas scene, were subjected to experiments for this paper. Six methods of dimensionality reduction (DR) were used for comparison experiments using different classifiers such as k-nearest neighbor (KNN) and support vector machine (SVM). The experimental results show that the ISS-WME algorithm can represent the HSI structure better than other methods, and effectively improves the classification accuracy of HSIs.

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Dimensionality Reduction of Hyperspectral Image Using Spatial-Spectral Regularized Sparse Hypergraph Embedding

Cited by 17 publications

References 48 publications

Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review

Dimensionality Reduction of Hyperspectral Images Based on Improved Spatial–Spectral Weight Manifold Embedding

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