Weighted Sparse Graph Based Dimensionality Reduction for Hyperspectral Images

Wei, He; Zhang, Hongyan; Zhang, Liangpei; Philips, Wilfried; Liao, Wenzhi

doi:10.1109/lgrs.2016.2536658

Cited by 45 publications

(10 citation statements)

References 19 publications

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“…Unfortunately, the converse is not true: sparsity does not always guarantee locality [56]. He et al [57] proposed a weighted sparse graph to overcome the drawback of sparse coding in SGE, where both the locality and sparsity structure of the training pixels are integrated.…”

Section: B Supervised Drmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

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669

266

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Recent advances in airborne and spaceborne hyperspectral imaging technology have provided end users with rich spectral, spatial, and temporal information, which make a plethora of applications for the analysis of large areas of the Earth surface feasible. However, a huge number of factors, such as high dimensions and size of the hyperspectral data, the lack of training samples, mixed pixels, light scattering mechanisms in the acquisition process, and different atmospheric and geometric distortions, make such data inherently nonlinear and complex, which poses extreme challenges for existing methodologies to effectively process and analyze the data sets. Hence, rigorous and innovative methodologies are required for hyperspectral image and signal processing and have become a center of attention for researchers worldwide. This paper offers a comprehensive tutorial/overview focusing specifically on hyperspectral data analysis, which is categorized into seven broad topics: classification, spectral unmixing, dimensionality reduction, resolution enhancement, hyperspectral image denoising and restoration, change detection, and fast computing. For each topic, we provide a synopsis of the state-of-the-art approaches and numerical results for validation and evaluation of different methodologies, followed by a discussion of future challenges and research directions.

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Section: B Supervised Drmentioning

confidence: 99%

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Ghamisi

Yokoya

et al. 2017

IEEE Geosci. Remote Sens. Mag.

Self Cite

669

266

View full text Add to dashboard Cite

show abstract

“…Recently, various graph-based algorithms are demonstrated to be effective for solving dimensionality reduction problems in high-dimensional data [26][27][28][29]. How to construct the similarity graph plays a vital role in these algorithms.…”

Section: Similarity Graph In Lpp and Sgdamentioning

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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“…Other related techniques include Discriminative Gaussian Process Latent Variable Model (DGPLVM) [47], Locally Weighted Discriminant Analysis (LWDA) [48], Multi-Feature Manifold Discriminant Analysis (MFMDA) [49], etc. Similarly, the representation based algorithms have also been introduced to supervised DR framework, such as Sparse Graph-based Discriminate Analysis (SGDA) [50], Weighted Sparse Graph-based Discriminate Analysis (WSGDA) [51], Collaborative Graph-based Discriminate Analysis (CGDA) [52], Laplacian regularized CGDA (LapCGDA) [53], Discriminant Analysis with Graph Learning (DAGL) [54], Graph-based Discriminant Analysis with Spectral Similarity (GDA-SS) [55], Local Geometric Structure Fisher Analysis (LGSFA) [56], Sparse and Low-Rank Graph-based Discriminant Analysis (SLGDA) [57], Kernel CGDA (KCGDA) [53], Laplacian Regularized Spatial-aware CGDA (LapSaCGDA) [58], etc. A good survey of these discriminant analysis models can be found in [31].…”

Section: Introductionmentioning

confidence: 99%

Gaussian Process Graph-Based Discriminant Analysis for Hyperspectral Images Classification

et al. 2019

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Dimensionality Reduction (DR) models are highly useful for tackling Hyperspectral Images (HSIs) classification tasks. They mainly address two issues: the curse of dimensionality with respect to spectral features, and the limited number of labeled training samples. Among these DR techniques, the Graph-Embedding Discriminant Analysis (GEDA) framework has demonstrated its effectiveness for HSIs feature extraction. However, most of the existing GEDA-based DR methods largely rely on manually tuning the parameters so as to obtain the optimal model, which proves to be troublesome and inefficient. Motivated by the nonparametric Gaussian Process (GP) model, we propose a novel supervised DR algorithm, namely Gaussian Process Graph-based Discriminate Analysis (GPGDA). Our algorithm takes full advantage of the covariance matrix in GP to constructing the graph similarity matrix in GEDA framework. In this way, more superior performance can be provided with the model parameters tuned automatically. Experiments on three real HSIs datasets demonstrate that the proposed GPGDA outperforms some classic and state-of-the-art DR methods.

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Weighted Sparse Graph Based Dimensionality Reduction for Hyperspectral Images

Cited by 45 publications

References 19 publications

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art

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

Gaussian Process Graph-Based Discriminant Analysis for Hyperspectral Images Classification

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