Tensor-Based Low-Rank Graph With Multimanifold Regularization for Dimensionality Reduction of Hyperspectral Images

An, Jinliang; Zhang, Xiangrong; Zhou, Huiyu; Jiao, Licheng

doi:10.1109/tgrs.2018.2835514

Cited by 40 publications

(26 citation statements)

References 46 publications

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“…(ii) Comparison methods: To evaluate the performance of the proposed method, several low rank and tensor based methods are selected as the comparison methods, including low-rank graph discriminant analysis (LGDA) [12], group tensor based low rank decomposition (GTLR) [26], tensor based low-rank representation (TLRR) [21], low rank tensor approximation(LRTA) [17] and tensor-based low rank graph with multi-manifold regularization(T-LGMR) [21]. In addition, the original spectral bands and classical PCA are also considered in this letter.…”

Section: Methodsmentioning

confidence: 99%

“…Tensor analysis is a multilinear algebra tool which needs no vectoring operation. Tensor analysis has been widely considered in hyperspectral image processing and achieved promising performance [16][17][18][19][20][21]. In tensor based methods, the spatial and spectral information are preserved simultaneously by representing hyperspectral images in the form of 3-order tensors.…”

Section: Introductionmentioning

confidence: 99%

“…The available methods usually calculate the optimal rank by converting the rank estimating issue to an iteration optimal problem with some constraints. However, this strategy is also time consuming and there are usually trade-off parameters that are difficult to be tuned [21].…”

Section: Introductionmentioning

confidence: 99%

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Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Lei

Song

et al. 2019

Remote Sensing

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Lei

Song

et al. 2019

Remote Sensing

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“…They have been successfully applied in military and civilian domains [2]. A lot of research works on HSI range from dimensionality reduction [3], spectral unmixing [4], HSI classification [5], [6] to detection task [7], [8], [9]. There are two application scenarios in the detection task: target detection and anomaly detection.…”

mentioning

confidence: 99%

Hyperspectral Anomaly Detection Based on Low-Rank Representation With Data-Driven Projection and Dictionary Construction

Zhang

Tang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral image anomaly detection is an increasingly important research topic in remote sensing images understanding and interpretation. Recently, low-rank representation-based methods have attracted extensive attention and achieved promising performances in hyperspectral anomaly detection. These methods assume that the hyperspectral data can be decomposed into two parts: the low-rank component representing the background and the residual part indicating the anomaly. In order to improve the separability of the background and anomaly, we propose a novel hyperspectral anomaly detection based on low-rank representation with dictionary construction and data-driven projection. To construct a robust dictionary that contains all categories of the background objects whilst excluding the anomaly's influence, we adopt a superpixel-based tensor low-rank decomposition method to generate a comprehensive and pure background dictionary. Considering the spectral redundancy in the hyperspectral data, data-driven projection is introduced to the low-rank representation to project the original data to a low-dimensional feature space to better separate the anomaly and the background. Experimental results on four real hyperspectral datasets show that the proposed anomaly detection method outperforms the other anomaly detectors.

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“…In addition, natural images are usually generated by the interaction of multiple factors related to scene structure, illumination and imaging [33]. Recently, tensor decomposition has shown great potentials for HSI classification [34][35][36], denosing [37], dimensionality reduction [38], hyperspectral and multispectral image fusion [39], target detection [40,41], spectral unmixing [42], etc. However, previous tensor factorization related studies rarely exploited hyperspectral and LiDAR data fusion and classification.…”

mentioning

confidence: 99%

Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

et al. 2019

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Hyperspectral and light detection and ranging (LiDAR) data fusion and classification has been an active research topic, and intensive studies have been made based on mathematical morphology. However, matrix-based concatenation of morphological features may not be so distinctive, compact, and optimal for classification. In this work, we propose a novel Coupled Higher-Order Tensor Factorization (CHOTF) model for hyperspectral and LiDAR data classification. The innovative contributions of our work are that we model different features as multiple third-order tensors, and we formulate a CHOTF model to jointly factorize those tensors. Firstly, third-order tensors are built based on spectral-spatial features extracted via attribute profiles (APs). Secondly, the CHOTF model is defined to jointly factorize the multiple higher-order tensors. Then, the latent features are generated by mode-n tensor-matrix product based on the shared and unshared factors. Lastly, classification is conducted by using sparse multinomial logistic regression (SMLR). Experimental results, conducted with two popular hyperspectral and LiDAR data sets collected over the University of Houston and the city of Trento, respectively, indicate that the proposed framework outperforms the other methods, i.e., different dimensionality-reduction-based methods, independent third-order tensor factorization based methods, and some recently proposed hyperspectral and LiDAR data fusion and classification methods.

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Tensor-Based Low-Rank Graph With Multimanifold Regularization for Dimensionality Reduction of Hyperspectral Images

Cited by 40 publications

References 46 publications

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Tensor Based Multiscale Low Rank Decomposition for Hyperspectral Images Dimensionality Reduction

Hyperspectral Anomaly Detection Based on Low-Rank Representation With Data-Driven Projection and Dictionary Construction

Coupled Higher-Order Tensor Factorization for Hyperspectral and LiDAR Data Fusion and Classification

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