Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Zhang, Shuzhen; Li, Shutao; Fu, Wei; Fang, Leiyuan

doi:10.3390/rs9020139

Cited by 71 publications

(46 citation statements)

References 66 publications

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“…By segmenting the HSIs to superpixels, it will be beneficial to exploit rich spatial information about the land surface [52], [32]. However, how to select an optimal value for the number of superpixels is a very challenging problem in actual applications [46].…”

Section: B Multiscale Extension Of Superpcamentioning

confidence: 99%

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

Jiang¹,

Ma²,

Chen³

et al. 2018

IEEE Trans. Geosci. Remote Sensing

276

174

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As an unsupervised dimensionality reduction method, principal component analysis (PCA) has been widely considered as an efficient and effective preprocessing step for hyperspectral image (HSI) processing and analysis tasks. It takes each band as a whole and globally extracts the most representative bands. However, different homogeneous regions correspond to different objects, whose spectral features are diverse. It is obviously inappropriate to carry out dimensionality reduction through a unified projection for an entire HSI. In this paper, a simple but very effective superpixelwise PCA approach, called SuperPCA, is proposed to learn the intrinsic lowdimensional features of HSIs. In contrast to classical PCA models, SuperPCA has four main properties. (1) Unlike the traditional PCA method based on a whole image, SuperPCA takes into account the diversity in different homogeneous regions, that is, different regions should have different projections.(2) Most of the conventional feature extraction models cannot directly use the spatial information of HSIs, while SuperPCA is able to incorporate the spatial context information into the unsupervised dimensionality reduction by superpixel segmentation.(3) Since the regions obtained by superpixel segmentation have homogeneity, SuperPCA can extract potential low-dimensional features even under noise. (4) Although SuperPCA is an unsupervised method, it can achieve competitive performance when compared with supervised approaches. The resulting features are discriminative, compact, and noise resistant, leading to improved HSI classification performance. Experiments on three public datasets demonstrate that the SuperPCA model significantly outperforms the conventional PCA based dimensionality reduction baselines for HSI classification, and some state-of-the-art feature extraction approaches. The Matlab source code is available at https://github.com/junjun-jiang/SuperPCA.

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Section: B Multiscale Extension Of Superpcamentioning

confidence: 99%

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

Jiang¹,

Ma²,

Chen³

et al. 2018

IEEE Trans. Geosci. Remote Sensing

276

174

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“…Over the past decade, superpixel segmentation methods have also been extended to HSI classification [42][43][44][45][46][47][48][49][50], aiming at making full use of spectral information and spatial structure in hyperspectral data. By the combination of different segmentation techniques with various classification methods, a number of approaches for HSI classification have been developed, such as ER with sparse representation [42,[51][52][53], SVM [54] or extreme learning machines [55], SLIC with multi-morphological method [56], SVM [57] or convolutional neural network [58] and so on. These HSI classification methods based on superpixel segmentation display good performance in experiments.…”

Section: Introductionmentioning

confidence: 99%

An Effective Classification Scheme for Hyperspectral Image Based on Superpixel and Discontinuity Preserving Relaxation

et al. 2019

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Hyperspectral image (HSI) classification is one of the most active topics in remote sensing. However, it is still a nontrivial task to classify the hyperspectral data accurately, since HSI always suffers from a large number of noise pixels, the complexity of the spatial structure of objects and the spectral similarity between different objects. In this study, an effective classification scheme for hyperspectral image based on superpixel and discontinuity preserving relaxation (DPR) is proposed to discriminate land covers of interest. A novel technique for measuring the similarity of a pair of pixels in HSI is suggested to improve the simple linear iterative clustering (SLIC) algorithm. Unlike the existing application of SLIC technique to HSI, the improved SLIC algorithm can be directly used to segment HSI into superpixels without using principal component analysis in advance, and is free of parameters. Furthermore, the proposed three-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Compared with the existing two-step classification framework, the use of DPR technology in preprocessing significantly improves the classification accuracy. The effectiveness of the proposed method is verified on three public real hyperspectral datasets. The comparison results of several competitive methods show the superiority of this scheme.

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“…For CR-based classification, the related methods usually assign a neighborhood/window to each pixel, and perform the representation of a test pixel jointly by its neighbouring pixels [21,[42][43][44][45]. In addition, there are other methods that consider incorporating the spatial information by appending a spatial-spectral term to the coding model of CR-based classification [11,25,46].…”

Section: Introductionmentioning

confidence: 99%

Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

Liu

Xiao

et al. 2017

IJGI

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Abstract:As a widely used classifier, sparse representation classification (SRC) has shown its good performance for hyperspectral image classification. Recent works have highlighted that it is the collaborative representation mechanism under SRC that makes SRC a highly effective technique for classification purposes. If the dimensionality and the discrimination capacity of a test pixel is high, other norms (e.g., 2 -norm) can be used to regularize the coding coefficients, except for the sparsity 1 -norm. In this paper, we show that in the kernel space the nonnegative constraint can also play the same role, and thus suggest the investigation of kernel fully constrained least squares (KFCLS) for hyperspectral image classification. Furthermore, in order to improve the classification performance of KFCLS by incorporating spatial-spectral information, we investigate two kinds of spatial-spectral methods using two regularization strategies: (1) the coefficient-level regularization strategy, and (2) the class-level regularization strategy. Experimental results conducted on four real hyperspectral images demonstrate the effectiveness of the proposed KFCLS, and show which way to incorporate spatial-spectral information efficiently in the regularization framework.

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Multiscale Superpixel-Based Sparse Representation for Hyperspectral Image Classification

Cited by 71 publications

References 66 publications

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

SuperPCA: A Superpixelwise PCA Approach for Unsupervised Feature Extraction of Hyperspectral Imagery

An Effective Classification Scheme for Hyperspectral Image Based on Superpixel and Discontinuity Preserving Relaxation

Classification of Hyperspectral Images Using Kernel Fully Constrained Least Squares

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