A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

Sun, Weiwei; Jiang, Man; Li, Weiyue; Liu, Yinnian

doi:10.3390/rs8030238

Cited by 33 publications

(14 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are four terms required to be computed in Equation (11). The first term is B(n) T B(n), which can be obtained by the following recursive equation…”

Section: Decomposition Of the Residual Multiplication Termmentioning

confidence: 99%

“…In recent years, sparse regression models were used to perform BS. For instance, Sun et al [9][10][11] uses a self-sparse regression (SSR) model to select bands. In an SSR problem, finding a new basis is equivalent to selecting the most representation bands in an HSI image.…”

Section: Introductionmentioning

confidence: 99%

“…Many different types of BS algorithms [2][3][4][5][6][7][8][9][10][11][12][13][14][15] have been proposed in the past two decades. Most of them make an assumption that the BS problem is an optimization problem, which maximizes or minimizes a pre-defined objective function that can measure the amount of information or inter-band redundancy contained by the currently selected bands.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

et al. 2018

View full text Add to dashboard Cite

Band selection (BS) is one of the important topics in hyperspectral image (HSI) processing. Many types of BS algorithms were proposed in the last decade. However, most of them were designed for off-line use. They can only be used with pre-collected data, and are sometimes ineffective for applications that require timeliness, such as disaster prevention or target detection. This paper proposes an online BS method that allows us obtain instant BS results in a progressive manner during HSI data transmission, which is carried out under band-interleaved-by-sample/pixel (BIS/BIP) format. Such a revolutionary method is called progressive sample processing of band selection (PSP-BS). In PSP-BS, BS can be done recursively pixel by pixel, so that the instantaneous BS can be achieved without waiting for all the pixels of an image. To develop a PSP-BS algorithm, we proposed PSP-OMPBS, which adopted the recursive version of a self-sparse regression BS method (OMPBS) as a native algorithm. The experiments conducted on two real hyperspectral images demonstrate that PSP-OMPBS can progressively output the BS with extremely low computing time. In addition, the convergence of BS results during transmission can be further accelerated by using a pre-defined pixel transmission sequence. Such a significant advantage not only allows BS to be done in a real-time manner for the future satellite data downlink, but also determines the BS results in advance, without waiting to receive every pixel of an image.

show abstract

“…There are four terms required to be computed in Equation (11). The first term is B(n) T B(n), which can be obtained by the following recursive equation…”

Section: Decomposition Of the Residual Multiplication Termmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Hyperspectral image (HSI) [1][2][3][4] contains hundreds of narrow bands, and has been extensively used in different application domains, such as forest monitoring and mapping [5,6], land-use classification [7,8], anomaly detection [9], endmember extraction [10] and environment monitoring [11]. Among those kinds of applications, supervised classification is a fundamental task and has been widely studied over the past decades [12,13].…”

Section: Introductionmentioning

confidence: 99%

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Hu¹,

He²,

Li³

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Active learning (AL) has been shown to be very effective in hyperspectral image (HSI) classification. It significantly improves the performance by selecting a small quantity of the most informative training samples to reduce the complexity of classification. Multiview AL (MVAL) can make the comprehensive analysis of both object characterization and sampling selection in AL by using various features of multiple views. However, the original MVAL cannot effectively exploit the spectral-spatial information by respecting the three-dimensional (3D) nature of the HSI and the query selection strategy in the MVAL is only based on the disagreement of multiple views. In this paper, we propose a 3D-Gabor inspired MVAL method for spectral-spatial HSI classification, which consists of two main steps. First, in the view generation step, we adopt a 3D-Gabor filter to generate multiple cubes with limited bands and utilize the feature assessment strategies to select cubes for constructing views. Second, in the sampling selection step, a novel method is proposed by using both internal and external uncertainty estimation (IEUE) of views. Specifically, we use the distributions of posterior probability to learn the "internal uncertainty" of each independent view, and adopt the inconsistencies between views to estimate the "external uncertainty". Classification accuracies of the proposed method for the four benchmark HSI datasets can be as high as 99.57%, 99.93%, 99.02%, 98.82%, respectively, demonstrating the improved performance as compared with other state-of-the-art methods.

show abstract

“…Due to the abundance of information contained in HSI, hyperspectral imaging has opened new avenues in remote sensing [1][2][3][4][5]. One of the most important tasks in HSI is pixel-oriented classification [6][7][8][9], where each pixel is labeled by one of the classes based on the training samples given for each class.…”

Section: Introductionmentioning

confidence: 99%

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Liu

2016

Remote Sensing

View full text Add to dashboard Cite

Abstract:Recently, sparse representation has yielded successful results in hyperspectral image (HSI) classification. In the sparse representation-based classifiers (SRCs), a more discriminative representation that preserves the spectral-spatial information can be exploited by treating the HSI as a whole entity. Based on this observation, a tensor block-sparsity based representation method is proposed for spectral-spatial classification of HSI in this paper. Unlike traditional vector/matrix-based SRCs, the proposed method consists of tensor block-sparsity based dictionary learning and class-dependent block sparse representation. By naturally regarding the HSI cube as a third-order tensor, small local patches centered at the training samples are extracted from the HSI to maintain the structural information. All the patches are then partitioned into a number of groups, on which a dictionary learning model is constructed with a tensor block-sparsity constraint. A test sample is also expressed as a small local patch and the block sparse representation is then performed in a class-wise manner to take advantage of the class label information. Finally, the category of the test sample is determined by using the minimal residual. Experimental results of two real-world HSIs show that our proposed method greatly improves the classification performance of SRC.

show abstract

A Symmetric Sparse Representation Based Band Selection Method for Hyperspectral Imagery Classification

Cited by 33 publications

References 50 publications

Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

Progressive Sample Processing of Band Selection for Hyperspectral Image Transmission

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Tensor Block-Sparsity Based Representation for Spectral-Spatial Hyperspectral Image Classification

Contact Info

Product

Resources

About