Special Issue "Dimensionality Reduction for Hyperspectral Imagery Analysis"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (30 August 2019).

Special Issue Editors

Prof. Hongjun Su
E-Mail Website
Guest Editor
School of Earth Science and Engineering, Hohai University; 8 Focheng West Road; Jiangning District; Nanjing 211100; China
Interests: Hyperspectral remote sensing; image analysis; machine learning; computational intelligence
Special Issues and Collections in MDPI journals
Dr. Yanfei Zhong
E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University; 129 Luoyu Road, Wuhan 430079, China
Interests: multi- and hyperspectral remote sensing data processing; high resolution image processing and scene analysis; and computational intelligence
Special Issues and Collections in MDPI journals
Dr. Xiangrong Zhang
E-Mail
Guest Editor
School of Artificial Intelligence, Xidian University; 2 Taibai South Road; Yanta District; Xi’an 710071; China
Interests: remote sensing image analysis and interpretation; machine learning; pattern recognition
Dr. Chen Chen
E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, The University of North Carolina at Charlotte, Charlotte, NC 28223
Interests: compressed sensing; signal and image processing; pattern recognition; computer vision; hyperspectral image analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleagues,

Dimensionality reduction for hyperspectral remote sensing plays an important role in scientific applications. With the rapid advance of hyperspectral imaging technology, a vast and ever-growing amount of remote sensing data (i.e., high dimensionality) is readily available. The emergence of hyperspectral remote sensing has brought about a paradigm shift in many fields (especially in the geosciences) of data analytics, such as image processing and geoscience applications; for instance, the popular machine learning has evolved into high dimensional remote sensing data for feature extraction or selection, and provided tremendous power for dimensionality reduction and further applications. Therefore, the primary goal of this Special Issue of Remote Sensing is to provide the opportunity for researchers to discuss the state-of-the-art and trends of theories, methodologies, techniques, and applications for the dimensionality reduction of hyperspectral remote sensing and geoscience understanding.

Topics of Interest:

This Special Issue aims to publish contributions reporting the most recent progress in dimensionality reduction for hyperspectral imagery analysis. The list of possible topics includes, but is not limited to, the following:

  • Hyperspectral remote sensing big data processing
  • High dimensional remote sensing image processing
  • Intrinsic dimension analysis
  • Information assessment in hyperspectral remote sensing
  • Feature extraction
  • Feature (band) selection
  • Feature optimization (swarm intelligence algorithms, e.g., genetic algorithms, particle swarm optimization, firefly algorithms, etc.)
  • Machine learning (e.g., deep learning, sparse representation, low rank representation, collaborative representation, manifold learning, etc.) for hyperspectral image analysis
  • Dimensionality reduction for further analysis (e.g., classification, segmentation, detection and recognition, etc.)
  • Applications of dimensionality reduction (e.g., urban, agriculture, environment, land cover, hydrology, forest, Earth surface processes, etc.)

Dr. Hongjun Su
Dr. Yanfei Zhong
Dr. Xiangrong Zhang
Dr. Chen Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Hyperspectral remote sensing
  • Intrinsic dimension analysis
  • Information assessment
  • Feature extraction
  • Feature (band) selection
  • Feature optimization
  • Machine learning

Published Papers (8 papers)

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Research

Open AccessArticle
Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction
Remote Sens. 2019, 11(12), 1485; https://doi.org/10.3390/rs11121485 - 22 Jun 2019
Cited by 1
Abstract
Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. [...] Read more.
Dimensionality reduction is an essential and important issue in hyperspectral image processing. With the advantages of preserving the spatial neighborhood information and the global structure information, tensor analysis and low rank representation have been widely considered in this field and yielded satisfactory performance. In available tensor- and low rank-based methods, how to construct appropriate tensor samples and determine the optimal rank of hyperspectral images along each mode are still challenging issues. To address these drawbacks, an unsupervised tensor-based multiscale low rank decomposition (T-MLRD) method for hyperspectral images dimensionality reduction is proposed in this paper. By regarding the raw cube hyperspectral image as the only tensor sample, T-MLRD needs no labeled samples and avoids the processing of constructing tensor samples. In addition, a novel multiscale low rank estimating method is proposed to obtain the optimal rank along each mode of hyperspectral image which avoids the complicated rank computing. Finally, the multiscale low rank feature representation is fused to achieve dimensionality reduction. Experimental results on real hyperspectral datasets demonstrate the superiority of the proposed method over several state-of-the-art approaches. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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Open AccessArticle
Discovering the Representative Subset with Low Redundancy for Hyperspectral Feature Selection
Remote Sens. 2019, 11(11), 1341; https://doi.org/10.3390/rs11111341 - 04 Jun 2019
Abstract
In this paper, a novel unsupervised band selection (BS) criterion based on maximizing representativeness and minimizing redundancy (MRMR) is proposed for selecting a set of informative bands to represent the whole hyperspectral image cube. The new selection criterion is denoted as the MRMR [...] Read more.
In this paper, a novel unsupervised band selection (BS) criterion based on maximizing representativeness and minimizing redundancy (MRMR) is proposed for selecting a set of informative bands to represent the whole hyperspectral image cube. The new selection criterion is denoted as the MRMR selection criterion and the associated BS method is denoted as the MRMR method. The MRMR selection criterion can evaluate the band subset’s representativeness and redundancy simultaneously. For one band subset, its representativeness is estimated by using orthogonal projection (OP) and its redundancy is measured by the average of the Pearson correlation coefficients among the bands in this subset. To find the satisfactory subset, an effective evolutionary algorithm, i.e., the immune clone selection (ICS) algorithm, is applied as the subset searching strategy. Moreover, we further introduce two effective tricks to simplify the computation of the representativeness metric, thus the computational complexity of the proposed method is reduced significantly. Experimental results on different real-world datasets demonstrate that the proposed method is very effective and its selected bands can obtain good classification performances in practice. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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Open AccessArticle
Joint Local Block Grouping with Noise-Adjusted Principal Component Analysis for Hyperspectral Remote-Sensing Imagery Sparse Unmixing
Remote Sens. 2019, 11(10), 1223; https://doi.org/10.3390/rs11101223 - 23 May 2019
Cited by 4
Abstract
Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression. In a spatial regularized sparse unmixing model, spatial [...] Read more.
Spatial regularized sparse unmixing has been proved as an effective spectral unmixing technique, combining spatial information and standard spectral signatures known in advance into the traditional spectral unmixing model in the form of sparse regression. In a spatial regularized sparse unmixing model, spatial consideration acts as an important role and develops from local neighborhood pixels to global structures. However, incorporating spatial relationships will increase the computational complexity, and it is inevitable that some negative influences obtained by inaccurate estimated abundances’ spatial correlations will reduce the accuracy of the algorithms. To obtain a more reliable and efficient spatial regularized sparse unmixing results, a joint local block grouping with noise-adjusted principal component analysis for hyperspectral remote-sensing imagery sparse unmixing is proposed in this paper. In this work, local block grouping is first utilized to gather and classify abundant spatial information in local blocks, and noise-adjusted principal component analysis is used to compress these series of classified local blocks and select the most significant ones. Then the representative spatial correlations are drawn and replace the traditional spatial regularization in the spatial regularized sparse unmixing method. Compared with total variation-based and non-local means-based sparse unmixing algorithms, the proposed approach can yield comparable experimental results with three simulated hyperspectral data cubes and two real hyperspectral remote-sensing images. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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Open AccessArticle
Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis
Remote Sens. 2019, 11(10), 1219; https://doi.org/10.3390/rs11101219 - 23 May 2019
Cited by 1
Abstract
Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA) on each homogeneous region is proposed to fully utilize the KPCA’s [...] Read more.
Dimensionality reduction (DR) is an important preprocessing step in hyperspectral image applications. In this paper, a superpixelwise kernel principal component analysis (SuperKPCA) method for DR that performs kernel principal component analysis (KPCA) on each homogeneous region is proposed to fully utilize the KPCA’s ability to acquire nonlinear features. Moreover, for the proposed method, the differences in the DR results obtained based on different fundamental images (the first principal components obtained by principal component analysis (PCA), KPCA, and minimum noise fraction (MNF)) are compared. Extensive experiments show that when 5, 10, 20, and 30 samples from each class are selected, for the Indian Pines, Pavia University, and Salinas datasets: (1) when the most suitable fundamental image is selected, the classification accuracy obtained by SuperKPCA can be increased by 0.06%–0.74%, 3.88%–4.37%, and 0.39%–4.85%, respectively, when compared with SuperPCA, which performs PCA on each homogeneous region; (2) the DR results obtained based on different first principal components are different and complementary. By fusing the multiscale classification results obtained based on different first principal components, the classification accuracy can be increased by 0.54%–2.68%, 0.12%–1.10%, and 0.01%–0.08%, respectively, when compared with the method based only on the most suitable fundamental image. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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Open AccessArticle
Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image Classification
Remote Sens. 2019, 11(6), 651; https://doi.org/10.3390/rs11060651 - 17 Mar 2019
Cited by 7
Abstract
Hyperspectral image (HSI) provides both spatial structure and spectral information for classification, but many traditional methods simply concatenate spatial features and spectral features together that usually lead to the curse-of-dimensionality and unbalanced representation of different features. To address this issue, a new dimensionality [...] Read more.
Hyperspectral image (HSI) provides both spatial structure and spectral information for classification, but many traditional methods simply concatenate spatial features and spectral features together that usually lead to the curse-of-dimensionality and unbalanced representation of different features. To address this issue, a new dimensionality reduction (DR) method, termed multi-feature manifold discriminant analysis (MFMDA), was proposed in this paper. At first, MFMDA explores local binary patterns (LBP) operator to extract textural features for encoding the spatial information in HSI. Then, under graph embedding framework, the intrinsic and penalty graphs of LBP and spectral features are constructed to explore the discriminant manifold structure in both spatial and spectral domains, respectively. After that, a new spatial-spectral DR model for multi-feature fusion is built to extract discriminant spatial-spectral combined features, and it not only preserves the similarity relationship between spectral features and LBP features but also possesses strong discriminating ability in the low-dimensional embedding space. Experiments on Indian Pines, Heihe and Pavia University (PaviaU) hyperspectral data sets demonstrate that the proposed MFMDA method performs significantly better than some state-of-the-art methods using only single feature or simply stacking spectral features and spatial features together, and the classification accuracies of it can reach 95.43%, 97.19% and 96.60%, respectively. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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Open AccessArticle
Locally Weighted Discriminant Analysis for Hyperspectral Image Classification
Remote Sens. 2019, 11(2), 109; https://doi.org/10.3390/rs11020109 - 09 Jan 2019
Cited by 3
Abstract
A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant [...] Read more.
A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant analysis (LDA) is a popular dimensionality reduction method for HSI classification, which assumes all the samples obey the same distribution. However, different samples may have different contributions in the computation of scatter matrices. To address the problem of feature redundancy, a new supervised HSI classification method based on locally weighted discriminant analysis (LWDA) is presented. The proposed LWDA method constructs a weighted discriminant scatter matrix model and an optimal projection matrix model for each training sample, which is on the basis of discriminant information and spatial-spectral information. For each test sample, LWDA searches its nearest training sample with spatial information and then uses the corresponding projection matrix to project the test sample and all the training samples into a low-dimensional feature space. LWDA can effectively preserve the spatial-spectral local structures of the original HSI data and improve the discriminating power of the projected data for the final classification. Experimental results on two real-world HSI datasets show the effectiveness of the proposed LWDA method compared with some state-of-the-art algorithms. Especially when the data partition factor is small, i.e., 0.05, the overall accuracy obtained by LWDA increases by about 20 % for Indian Pines and 17 % for Kennedy Space Center (KSC) in comparison with the results obtained when directly using the original high-dimensional data. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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Open AccessArticle
Unsupervised Feature Selection Based on Ultrametricity and Sparse Training Data: A Case Study for the Classification of High-Dimensional Hyperspectral Data
Remote Sens. 2018, 10(10), 1564; https://doi.org/10.3390/rs10101564 - 29 Sep 2018
Cited by 2
Abstract
In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only [...] Read more.
In this paper, we investigate the potential of unsupervised feature selection techniques for classification tasks, where only sparse training data are available. This is motivated by the fact that unsupervised feature selection techniques combine the advantages of standard dimensionality reduction techniques (which only rely on the given feature vectors and not on the corresponding labels) and supervised feature selection techniques (which retain a subset of the original set of features). Thus, feature selection becomes independent of the given classification task and, consequently, a subset of generally versatile features is retained. We present different techniques relying on the topology of the given sparse training data. Thereby, the topology is described with an ultrametricity index. For the latter, we take into account the Murtagh Ultrametricity Index (MUI) which is defined on the basis of triangles within the given data and the Topological Ultrametricity Index (TUI) which is defined on the basis of a specific graph structure. In a case study addressing the classification of high-dimensional hyperspectral data based on sparse training data, we demonstrate the performance of the proposed unsupervised feature selection techniques in comparison to standard dimensionality reduction and supervised feature selection techniques on four commonly used benchmark datasets. The achieved classification results reveal that involving supervised feature selection techniques leads to similar classification results as involving unsupervised feature selection techniques, while the latter perform feature selection independently from the given classification task and thus deliver generally versatile features. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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Open AccessArticle
Hyperspectral Target Detection via Adaptive Information—Theoretic Metric Learning with Local Constraints
Remote Sens. 2018, 10(9), 1415; https://doi.org/10.3390/rs10091415 - 06 Sep 2018
Cited by 8
Abstract
By using the high spectral resolution, hyperspectral images (HSIs) provide significant information for target detection, which is of great interest in HSI processing. However, most classical target detection methods may only perform well based on certain assumptions. Simultaneously, using limited numbers of target [...] Read more.
By using the high spectral resolution, hyperspectral images (HSIs) provide significant information for target detection, which is of great interest in HSI processing. However, most classical target detection methods may only perform well based on certain assumptions. Simultaneously, using limited numbers of target samples and preserving the discriminative information is also a challenging problem in hyperspectral target detection. To overcome these shortcomings, this paper proposes a novel adaptive information-theoretic metric learning with local constraints (ITML-ALC) for hyperspectral target detection. The proposed method firstly uses the information-theoretic metric learning (ITML) method as the objective function for learning a Mahalanobis distance to separate similar and dissimilar point-pairs without certain assumptions, needing fewer adjusted parameters. Then, adaptively local constraints are applied to shrink the distances between samples of similar pairs and expand the distances between samples of dissimilar pairs. Finally, target detection decision can be made by considering both the threshold and the changes between the distances before and after metric learning. Experimental results demonstrate that the proposed method can obviously separate target samples from background ones and outperform both the state-of-the-art target detection algorithms and the other classical metric learning methods. Full article
(This article belongs to the Special Issue Dimensionality Reduction for Hyperspectral Imagery Analysis)
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