Discriminative spatial-spectral manifold embedding for hyperspectral image classification

Zhou, Langming; Zhang, Xiaohu

doi:10.1080/2150704x.2015.1069904

Cited by 11 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The subspace division method based on the correlation coefficient is referred to as the correlation, and the subspace division method based on Euclidean distance is referred to as the Euclidean distance. The correlation coefficient r xy between bands x and y is defined [33,38] as:…”

Section: Correlation-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A Spatial–Spectral Combination Method for Hyperspectral Band Selection

et al. 2022

View full text Add to dashboard Cite

Hyperspectral images are characterized by hundreds of spectral bands and rich information. However, there exists a large amount of information redundancy among adjacent bands. In this study, a spatial–spectral combination method for hyperspectral band selection (SSCBS) is proposed to reduce information redundancy. First, the hyperspectral image is automatically divided into subspaces. Seven algorithms classified as four types are executed and compared. The means algorithm is the most suitable for subspace division of the input hyperspectral image, with the calculation being the fastest. Then, for each subspace, the spatial–spectral combination method is adopted to select the best band. The band with the maximum information and more prominent characteristics between the adjacent bands is selected. The parameters of Euclidean distance and spectral angle parameters are used to measure the intraclass correlation and interclass spectral specificity, respectively. Weight coefficient quantifying the intrinsic spatial–spectral relationship of pixels are constructed, and then the optimal bands are selected by a combination of the weight coefficients and the information entropy. Moreover, an automatic method is proposed in this paper to provide an appropriate number of band sets, which is out of consideration for existing research. The experimental results show, as compared to other competing methods, that the SSCBS approach has the highest classification accuracy on the three benchmark datasets and takes less computation time. These demonstrate that the proposed SSCBS achieves satisfactory performance against state-of-the-art algorithms.

show abstract

Section: Correlation-based Methodsmentioning

confidence: 99%

“…Huang et al [32] proposed a spatial-spectral manifold reconstruction preserving the embedding (SSMRPE) method. Zhou et al [33] proposed a spatial-spectral feature dimensionality reduction algorithm based on manifold learning. Zhao et al [34] proposed a spectralspatial feature-based classification (SSFC) framework.…”

Section: Introductionmentioning

confidence: 99%

A Spatial–Spectral Combination Method for Hyperspectral Band Selection

et al. 2022

View full text Add to dashboard Cite

show abstract

“…However, discriminant information from training samples is ignored. Zhou and Zhang [43] proposed discriminant spatial-spectral manifold embedding (DSSME) algorithm, in which the adjacency graph is constructed based on discriminant information from the available training samples. Unfortunately, there are two parameters, nearest neighbors size k and the kernel parameter δ, are difficult to determine in the Laplacian eigenmaps.…”

Section: Introductionmentioning

confidence: 99%

Incremental Graph Embedding Based on Spatial-Spectral Neighbors for Hyperspectral Image Classification

Cheng

Wang

et al. 2018

IEEE Access

View full text Add to dashboard Cite

Recently, graph embedding-based methods have been developed in dimensionality reduction (DR) and classification of hyperspectral image (HSI). The key step for graph embedding methods is the construction of graph. The commonly used method is to manually choose nearest neighbors and then, compute edge weights using the spectral feature. However, the adjacency graph is inappropriate due to the negligence of spatial information. What is more, the construction of graph only takes training samples or k nearest neighbors into account, which may lead to unsuitable graph representation. In this paper, we propose a novel incremental graph embedding (IGE) algorithm to construct a spatial-spectral neighbor graph for the HSI classification. The IGE can spread the discrimination information contained in training samples to their neighbors until each testing sample has a pseudo label. The spatial affinity weights between unlabeled data points and their labeled neighbors are calculated according to the construction strategies of the spatialspectral neighbor graph. The pseudo label of the unlabeled data point is determined based on the maximum spatial affinity weights. Moreover, three weight strategies are designed for those samples nearby the decision boundary to improve the separability of different classes. In addition, the window size of spatial neighbors is able to be adjusted adaptively according to whether labeled data points in spatial neighbors exit. Experimental results on two datasets, Indian Pine and Pavia University have demonstrated that our algorithm is remarkably superior to other conventional DR algorithms on improving classification performance.

show abstract

Hyperspectral Image Classification with Polynomial Laplacian Embedding

Zhang

Fan

et al. 2016

Intelligent Computing Methodologies

View full text Add to dashboard Cite

Discriminative spatial-spectral manifold embedding for hyperspectral image classification

Cited by 11 publications

References 21 publications

A Spatial–Spectral Combination Method for Hyperspectral Band Selection

A Spatial–Spectral Combination Method for Hyperspectral Band Selection

Incremental Graph Embedding Based on Spatial-Spectral Neighbors for Hyperspectral Image Classification

Hyperspectral Image Classification with Polynomial Laplacian Embedding

Contact Info

Product

Resources

About