Hyperspectral Image Classification Using Weighted Joint Collaborative Representation

Xiong, Mingming; Ran, Qiong; Li, Wei; Zou, Jinyi; Du, Qian

doi:10.1109/lgrs.2015.2388703

Cited by 57 publications

(5 citation statements)

References 17 publications

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“…The joint collaborative representation (JCR) method (Li & Du, 2014) is an extend version of NRS which incorporates the contextual features within the classification process. The weighted JCR (WJCR) method (Xiong, Ran, Li, Zou, & Du, 2015) extends JCR with assigning different weights to the surrounding pixels in a neighborhood local window. All of NRS, JCR and WJCR methods use the collaborative representation for classification of hyperspectral image in a supervised manner for a multi-class problem.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection using morphology-based collaborative representation in hyperspectral imagery

Imani

2018

European Journal of Remote Sensing

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A nonparametric anomaly detection method is proposed in this paper which does not consider any probability density function for data, and so can work well for real hyperspectral image containing complicated and non-normal background. Since the predominant part of an image is composed by background pixels and because of similarity of neighboring pixels in a local region, each background pixel can be approximated from its surrounding samples. To this end, the collaborative representation with a simple and closed form solution is used. To more benefit from the spatial information of hyperspectral image, the morphological filters are applied for extraction of contextual features and a decision fusion rule is used to utilize the spatial information of all principal components of data. The experimental results show the superior performance of the proposed detector compared to several state-of-the-art anomalous target detection methods. The area under ROC curve (AUC) values achieved by the proposed detector are 95.53, 96.34 and 99.72 for San Diego hyperspectral image, Indian hyperspectral image and the WorldView-2 multispectral image, respectively.

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Section: Introductionmentioning

confidence: 99%

Anomaly detection using morphology-based collaborative representation in hyperspectral imagery

Imani

2018

European Journal of Remote Sensing

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“…By setting a constant window scale size and averaging the nearby data information of training and test samples, Li et al [44] proposed the joint cooperative representation model. Building upon the joint cooperative representation model, Xiong et al [45] proposed the weighted joint cooperative representation algorithm by assigning different weight values to the selected nearby information using the Gaussian kernel function. Yang et al [46] considered the extraction of multiscale neighborhood information, the construction of local adaptive dictionaries, and the incorporation of complementary information in the classification process, proposing multiscale joint collaborative representation based on local adaptive dictionaries.…”

Section: Collaborative Representationmentioning

confidence: 99%

Hyperspectral Image Classification Using Geodesic Spatial–Spectral Collaborative Representation

Zheng,

Xiong,

et al. 2023

Electronics

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With the continuous advancement of remote sensing technology, the information encapsulated within hyperspectral images has become increasingly enriched. The effective and comprehensive utilization of spatial and spectral information to achieve the accurate classification of hyperspectral images presents a significant challenge in the domain of hyperspectral image processing. To address this, this paper introduces a novel approach to hyperspectral image classification based on geodesic spatial–spectral collaborative representation. It introduces geodesic distance to extract spectral neighboring information from hyperspectral images and concurrently employs Euclidean distance to extract spatial neighboring information. By integrating collaborative representation with spatial–spectral information, the model is constructed. The collaborative representation coefficients are obtained by solving the model to reconstruct the testing samples, leading to the classification results derived from the minimum reconstruction residuals. Finally, with comparative experiments conducted on three classical hyperspectral image datasets, the effectiveness of the proposed method is substantiated. On the Indian Pines dataset, the proposed algorithm achieved overall accuracy (OA) of 91.33%, average accuracy (AA) of 93.81%, and kappa coefficient (Kappa) of 90.13%. In the case of the Salinas dataset, OA was 95.62%; AA was 97.30%; and Kappa was 93.84%. Lastly, on the PaviaU dataset, OA stood at 95.77%; AA was 94.13%; and Kappa was 94.38%.

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“…Hence, it deserves further exploration to combine the spectral information and the spatial information. Previous studies utilized the dual-channel dilated convolutional neural network [65], the multiplekernel-based classifiers [66], the weighted joint collaborative representation [67,68], and the three-dimensional dilated convolution residual neural network [69] methods for hyperspectral image classification. It is found that hyperspectral image classification algorithms based on the fusion method may become popular in the future [70], while how to effectively combine the multidimensional information to improve the classification efficiency and accuracy will be a significant problem that needs to be solved.…”

Section: Grottoes and Muralsmentioning

confidence: 99%

Review on Analysis Methods Enabled by Hyperspectral Imaging for Cultural Relic Conservation

Pei,

Huang,

Zhou

2023

Photonics

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In this review, the conservation methods for various types of cultural relics enabled by hyperspectral imaging are summarized, and the hyperspectral cameras and techniques utilized in the process from data acquisition to analyzation are introduced. Hyperspectral imaging is characterized by non-contact detection, broadband, and high resolution, which are of great significance to the non-destructive investigation of cultural relics. However, owing to the wide variety of cultural relics, the utilized equipment and methods vary greatly in the investigations of the associated conservation. Previous studies generally select a single type of cultural relic for conservation. That is, seldom study has focused on the application of hyperspectral techniques to generalized conservation methods that are simultaneously suitable for different types of cultural relics. Hence, some widely used hyperspectral cameras and imaging systems are introduced first. Subsequently, according to the previous investigations, the methods used for image acquisition, image correction, and data dimensionality reduction in hyperspectral techniques are described. Thirdly, a summary of methods in cultural relic conservation based on hyperspectral techniques is presented, which involves pigments, grottoes and murals, and painting and calligraphy. Later, some challenges and potential development prospects in hyperspectral-based methods are discussed for future study. Finally, the conclusions are given.

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Hyperspectral Image Classification Using Weighted Joint Collaborative Representation

Cited by 57 publications

References 17 publications

Anomaly detection using morphology-based collaborative representation in hyperspectral imagery

Anomaly detection using morphology-based collaborative representation in hyperspectral imagery

Hyperspectral Image Classification Using Geodesic Spatial–Spectral Collaborative Representation

Review on Analysis Methods Enabled by Hyperspectral Imaging for Cultural Relic Conservation

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