Random Subspace-Based <i>k</i>-Nearest Class Collaborative Representation for Hyperspectral Image Classification

Su, Hongjun; Yu, Yang; Wu, Zhaoyue; Du, Qian

doi:10.1109/tgrs.2020.3029578

Cited by 34 publications

(11 citation statements)

References 41 publications

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“…After clustering, any fragments with area less than a predefined threshold are selectively deleted at each iteration process, and then the surrounding pixels of the seeds are merged into a larger superpixel. Each seed point is moved in a (3 × 3) neighborhood until the gradient of the corresponding position is the lowest, so as to prevent seed points from being located on the boundary of the image and affect the overall effect of clustering [33], [48], [49].…”

Section: A Hsi Representation Using Superpixel Segmentationmentioning

confidence: 99%

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Jia

Pan

et al. 2022

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

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Sparse unmixing with a semi-supervised fashion has been applied to hyperspectral remote sensing imagery. However, the imprecise spatial contextual information, the lack of global feature and the high mutual coherences of a spectral library greatly limit the performance of sparse unmixing. In order to address these prominent problems, a new paradigm to characterize sparse hyperspectral unmixing is proposed, namely, the superpixel-based weighted collaborative sparse regression and reweighted low-rank representation unmixing (SBWCRLRU). In the method, the weighted collaborative sparse regression explores the pixels shared the same support set to help the sparsity of abundance fraction, and the reweighted low rank representation minimizes the rank of the abundance matrix to promote the spatial consistency of the image. Meanwhile, superpixel segmentation is adopted to cluster the pixels into different spatial homogeneous regions to further improve the unmixing performance. Extensive experiments results conducted on both synthetic and real data demonstrate the effectiveness of the proposed SBWCRLRU. It can not only improve the performance of hyperspectral unmixing but also outperform the existing sparse unmixing approaches.

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Section: A Hsi Representation Using Superpixel Segmentationmentioning

confidence: 99%

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Jia

Pan

et al. 2022

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

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“…Given the training set X and the testing set Y, the linear combination of training samples X can represent the approximation 𝒚𝒚 � of a testing sample y in CRC as [67]:…”

Section: A Cr-based Classifiers Using In Desmentioning

confidence: 99%

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

Lü

et al. 2022

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

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Recently, a series of collaborative representation (CR) methods have attracted much attention for hyperspectral images classification. In this paper, two CR-based dynamic ensemble selection (DES) methods using multi-view kernel collaborative subspace clustering (MVKCSC) and random subspace multi-view kernel collaborative subspace clustering (RSMVKCSC) are proposed. In order to combine spectral and spatial information to construct a region of competence (RoC), the multi-view learning strategy is used in the general DES method. Compared with traditional clustering methods, the MVC can more effectively utilize multi-feature information. Moreover, a new method of constructing the Laplacian matrix using kernel collaborative representation coefficients is proposed for clustering based on subspace clustering and CR theory. This method is called MVKCSC, which can obtain the clustering results by using kernel CR self-representation coefficients. In addition, to increase diversity of samples, the random subspace method (RSM) and MVKCSC are combined for RMVKCSC. Moreover, the algorithm can obtain better clustering results by constraining samples and features simultaneously. The effectiveness of the proposed methods is validated using three hyperspectral data sets with few samples. The experimental results show that both DES-MVKCSC and DES-RSMVKCSC outperform their single classifier counterparts. In particular, the proposed methods provide superior performance compared with the state-of-the-art DES methods.

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“…A locally adaptive form of nearest neighbor classification (LANN) is proposed here to upgrade the obscenity of dimensionality [17]. An effective metric is used here to compute neighborhoods which determines the local decision boundaries from centroid information, and then shrink neighborhoods in directions orthogonal to these local decision boundaries, and extend them parallel to the boundaries [18][19] [20].…”

Section: Introductionmentioning

confidence: 99%

An Improved K-Nearest Neighbor Algorithm for Pattern Classification

Sultana¹,

Ferdousi²,

Tasnim³

et al. 2022

IJACSA

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This paper proposed a "Locally Adaptive K-Nearest Neighbor (LAKNN) algorithm" for pattern exploration problem to enhance the obscenity of dimensionality. To compute neighborhood local linear discriminant analysis is an effective metric which determines the local decision boundaries from centroid information. KNN is a novel approach which uses in many classifications problem of data mining and machine learning. KNN uses class conditional probabilities for unfamiliar pattern. For limited training data in high dimensional feature space this hypothesis is unacceptable due to disfigurement of high dimensionality. To normalize the feature value of dissimilar metrics, Standard Euclidean Distance is used in KNN which s misguide to find a proper subset of nearest points of the pattern to be predicted. To overcome the effect of high dimensionality LANN uses a new variant of Standard Euclidian Distance Metric. A flexible metric is estimated for computing neighborhoods based on Chi-squared distance analysis. Chi-squared metric is used to ascertains most significant features in finding k-closet points of the training patterns. This paper also shows that LANN outperformed other four different models of KNN and other machine-learning algorithm in both training and accuracy.

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Random Subspace-Based k-Nearest Class Collaborative Representation for Hyperspectral Image Classification

Cited by 34 publications

References 41 publications

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Superpixel-Based Weighted Collaborative Sparse Regression and Reweighted Low-Rank Representation for Hyperspectral Image Unmixing

Dynamic Ensemble Learning With Multi-View Kernel Collaborative Subspace Clustering for Hyperspectral Image Classification

An Improved K-Nearest Neighbor Algorithm for Pattern Classification

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