Multiple Feature Kernel Sparse Representation Classifier for Hyperspectral Imagery

Gan, Le; Xia, Junshi; Du, Peijun; Chanussot, Jocelyn

doi:10.1109/tgrs.2018.2814781

Cited by 34 publications

(15 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And it utilized superpixel segmentation to generate adaptive homogeneous regions. Gan et al [20] incorporated multiple types of features, which helps so much for HSI classification task, into a kernel sparse representation classifier (KSRC). In addition, Fang et al [21] proposed a multiscale adaptive sparse representation, which effectively integrated contextual feature at multiple scales by an adaptive sparse technique.…”

Section: A Sr-based Methodsmentioning

confidence: 99%

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Wang

2019

IEEE Trans. Geosci. Remote Sensing

208

View full text Add to dashboard Cite

Hyperspectral image (HSI) classification, which aims to assign an accurate label for hyperspectral pixels, has drawn great interest in recent years. Although low rank representation (LRR) has been used to classify HSI, its ability to segment each class from the whole HSI data has not been exploited fully yet. LRR has a good capacity to capture the underlying lowdimensional subspaces embedded in original data. However, there are still two drawbacks for LRR. First, LRR does not consider the local geometric structure within data, which makes the local correlation among neighboring data easily ignored. Second, the representation obtained by solving LRR is not discriminative enough to separate different data. In this paper, a novel locality and structure regularized low rank representation (LSLRR) model is proposed for HSI classification. To overcome the above limitations, we present locality constraint criterion (LCC) and structure preserving strategy (SPS) to improve the classical LRR. Specifically, we introduce a new distance metric, which combines both spatial and spectral features, to explore the local similarity of pixels. Thus, the global and local structures of HSI data can be exploited sufficiently. Besides, we propose a structure constraint to make the representation have a near block-diagonal structure. This helps to determine the final classification labels directly. Extensive experiments have been conducted on three popular HSI datasets. And the experimental results demonstrate that the proposed LSLRR outperforms other state-of-the-art methods.Index Terms-Hyperspectral image classification, low rank representation, block-diagonal structure.

show abstract

Section: A Sr-based Methodsmentioning

confidence: 99%

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Wang

2019

IEEE Trans. Geosci. Remote Sensing

208

View full text Add to dashboard Cite

show abstract

“…The class label with the minimal representative error will be assigned to the unlabeled samples. Some researchers extended the SRC-based methods to utilize spatial information in hyperspectral images, e.g., joint SRC [27,28], kernel SRC [29][30][31], adaptive SRC [32,33], etc. In addition, there was also some work that used multi-objective optimization to improve SRC directly [34,35].…”

Section: Introductionmentioning

confidence: 99%

A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification

Cui

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult. In this paper, we propose a novel sample pseudo-labeling method based on sparse representation (SRSPL) for hyperspectral image classification, in which sparse representation is used to select the purest samples to extend the training set. The proposed method consists of the following three steps. First, intrinsic image decomposition is used to obtain the reflectance components of hyperspectral images. Second, hyperspectral pixels are sparsely represented using an overcomplete dictionary composed of all training samples. Finally, information entropy is defined for the vectorized sparse representation, and then the pixels with low information entropy are selected as pseudo-labeled samples to augment the training set. The quality of the generated pseudo-labeled samples is evaluated based on classification accuracy, i.e., overall accuracy, average accuracy, and Kappa coefficient. Experimental results on four real hyperspectral data sets demonstrate excellent classification performance using the new added pseudo-labeled samples, which indicates that the generated samples are of high confidence.

show abstract

“…For improving the classification accuracy, effective fusion of spectral and spatial features in the SRC-based classification framework have attracted increasing attention. Most of current SRC-based methods [32][33][34][35] utilize adaptive strategies to estimate the sparse coefficients and determine the label of the test pixel by the sum of residuals from all extracted features. In [32], a collaborative representation-based multitask learning framework is introduced for fusion of multiple extracted features, where the significance of each feature is represented by an adaptive weight.…”

Section: Introductionmentioning

confidence: 99%

“…In [34], a multiple feature adaptive SRC framework (MFASR) has been proposed, where the generated sparse coefficients are obtained adaptively to keep the feature-specific pattern for multiple feature learning. Moreover, the similar kernel version of the multiple feature SRC [35] has also been introduced and shown the significance of the non-linear separability. Although these approaches have shown relative good performance, the mechanism for fusion of multiple features needs be further analysed to derive a more robust strategy.…”

Section: Introductionmentioning

confidence: 99%

Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images

et al. 2019

View full text Add to dashboard Cite

To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology.

show abstract

Multiple Feature Kernel Sparse Representation Classifier for Hyperspectral Imagery

Cited by 34 publications

References 57 publications

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

Locality and Structure Regularized Low Rank Representation for Hyperspectral Image Classification

A Sparse Representation-Based Sample Pseudo-Labeling Method for Hyperspectral Image Classification

Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images

Contact Info

Product

Resources

About