Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

Du, Weibao; Qiang, Wenwen; Liu, Meng; Hou, Qiuling; Ling, Zhen; Jing, Ling

doi:10.1080/01431161.2017.1415480

Cited by 23 publications

(10 citation statements)

References 20 publications

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“…To extract low-dimensional spatial-spectral joint features, a objective function should be designed to preserve the local neighborhood as well as compact the samples with interclass hypergraph and separate the samples with interclass hypergraph simultaneously. Therefore, Equations (25), (28), and (29) are transformed into the following optimization function:…”

Section: Spatial-spectral Hypergraph Embeddingmentioning

confidence: 99%

“…In [28], discriminant hyper-Laplacian projection (DHLP) was proposed using the hypergraph Laplacian for exploring the high-order geometric relationship of samples. Semi-supervised hypergraph embedding (SHGE) learns the discriminant structure form both labeled and unlabeled data, and it reveals the complex relationships of HSI pixels by building a semi-supervised hypergraph [29]. For analyzing the intrinsic properties of HSI pixels, a hypergraph Laplacian sparse coding method was constituted to capture the similarity among data points within the same hyperedge [30].…”

Section: Introductionmentioning

confidence: 99%

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

2019

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Many graph embedding methods are developed for dimensionality reduction (DR) of hyperspectral image (HSI), which only use spectral features to reflect a point-to-point intrinsic relation and ignore complex spatial-spectral structure in HSI. A new DR method termed spatial-spectral regularized sparse hypergraph embedding (SSRHE) is proposed for the HSI classification. SSRHE explores sparse coefficients to adaptively select neighbors for constructing the dual sparse hypergraph. Based on the spatial coherence property of HSI, a local spatial neighborhood scatter is computed to preserve local structure, and a total scatter is computed to represent the global structure of HSI. Then, an optimal discriminant projection is obtained by possessing better intraclass compactness and interclass separability, which is beneficial for classification. Experiments on Indian Pines and PaviaU hyperspectral datasets illustrated that SSRHE effectively develops a better classification performance compared with the traditional spectral DR algorithms.

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Section: Spatial-spectral Hypergraph Embeddingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…Based on the imaging procedure, HSI contains rich spectral and spatial features (Zhang and Du 2012;Natsagdorj et al 2017). For a large number of narrow bands, they have a strong correlation that results in massive redundant information in HSI (Du et al 2018;Mohanty, Happy, and Routray 2019a). Moreover, the pixels in HSI are mixed because of the impact of imaging condition.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the class label is very difficult to obtain in real application. Based on this, the Semisupervised Hyper-Graph Embedding (SHGE) method was developed to construct a feature learning model with the label and unlabel samples (Du et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Review on graph learning for dimensionality reduction of hyperspectral image

Zhang

Luo

2020

Geo-spatial Information Science

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Graph learning is an effective manner to analyze the intrinsic properties of data. It has been widely used in the fields of dimensionality reduction and classification for data. In this paper, we focus on the graph learning-based dimensionality reduction for a hyperspectral image. Firstly, we review the development of graph learning and its application in a hyperspectral image. Then, we mainly discuss several representative graph methods including two manifold learning methods, two sparse graph learning methods, and two hypergraph learning methods. For manifold learning, we analyze neighborhood preserving embedding and locality preserving projections which are two classic manifold learning methods and can be transformed into the form of a graph. For sparse graph, we introduce sparsity preserving graph embedding and sparse graph-based discriminant analysis which can adaptively reveal data structure to construct a graph. For hypergraph learning, we review binary hypergraph and discriminant hyper-Laplacian projection which can represent the high-order relationship of data.

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“…The unsupervised methods obtain the low dimensionality representation by mining the structure characters of original dataset and need no label samples and Principal Components Analysis (PCA) is the most famous unsupervised criterion. To jointly consider the advantages of supervised and unsupervised methods, the semi-supervised criterion utilizes the label information from a few labeled samples and the structure information extracted from a large number of unlabeled samples [3,4].…”

Section: Introductionmentioning

confidence: 99%

Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction

Song

Guo

et al. 2019

Remote Sensing

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Dimensionality reduction is of great importance which aims at reducing the spectral dimensionality while keeping the desirable intrinsic structure information of hyperspectral images. Tensor analysis which can retain both spatial and spectral information of hyperspectral images has caused more and more concern in the field of hyperspectral images processing. In general, a desirable low dimensionality feature representation should be discriminative and compact. To achieve this, a tensor discriminant analysis model via compact feature representation (TDA-CFR) was proposed in this paper. In TDA-CFR, the traditional linear discriminant analysis was extended to tensor space to make the resulting feature representation more informative and discriminative. Furthermore, TDA-CFR redefines the feature representation of each spectral band by employing the tensor low rank decomposition framework which leads to a more compact representation.

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Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

Cited by 23 publications

References 20 publications

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

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

Review on graph learning for dimensionality reduction of hyperspectral image

Tensor Discriminant Analysis via Compact Feature Representation for Hyperspectral Images Dimensionality Reduction

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