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

Xie, Fuding; Lei, Cunkuan; Yang, Jun; Cui, Jin

doi:10.3390/rs11101149

Cited by 9 publications

(9 citation statements)

References 62 publications

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“…Based on a novel sorting strategy and the suggested measurement, SLIC has been improved so that it can be straightly used to partition HSI into superpixels, without consulting PCA and requiring parameters. Compared with our earlier improvement of SLIC [28], the improved SLIC here has good computability and understanding. Furthermore, motivated by the idea of local mean-based pseudo nearest neighbor (LMPNN) rule, we also define a new metric to measure the similarity between a pair of superpixels.…”

Section: Introductionmentioning

confidence: 86%

“…This, to some extent, expands effectively the space of application of the proposed method. Compared with our previous work of the improvement of SLIC [28], the improved SLIC algorithm suggested in this study has good computability and is easy to understand. Obviously, the improved SLIC method can be used straightforwardly for superpixel segmentation of hyperspectral data with arbitrary dimensionality.…”

Section: Appl Sci 2020 10 463 4 Of 15mentioning

confidence: 93%

“…Lately, spatial structure information has been gradually taken into account in some pixel-based classification approaches [13][14][15][16][17][18][19][20], aiming at getting better classification results. Generally speaking, the purpose of introducing spatial information into the process of classification can be roughly understood as denoising HSI in preprocessing [21][22][23][24], defining the novel similarity between a pair of pixels [25,26], reducing dimensionality [27], improving the classification map in post-processing [28][29][30], or their combinations. From the point of view of classification accuracy, the existing literatures show that some spectral-spatial HSI classification methods do have good classification performance.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

et al. 2020

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Although superpixel segmentation provides a powerful tool for hyperspectral image (HSI) classification, it is still a challenging problem to classify an HSI at superpixel level because of the characteristics of adaptive size and shape of superpixels. Furthermore, these characteristics of superpixels along with the appearance of noisy pixels makes it difficult to appropriately measure the similarity between two superpixels. Under the assumption that pixels within a superpixel belong to the same class with a high probability, this paper proposes a novel spectral–spatial HSI classification method at superpixel level (SSC-SL). Firstly, a simple linear iterative clustering (SLIC) algorithm is improved by introducing a new similarity and a ranking technique. The improved SLIC, specifically designed for HSI, can straightly segment HSI with arbitrary dimensionality into superpixels, without consulting principal component analysis beforehand. In addition, a superpixel-to-superpixel similarity is newly introduced. The defined similarity is independent of the shape of superpixel, and the influence of noisy pixels on the similarity is weakened. Finally, the classification task is accomplished by labeling each unlabeled superpixel according to the nearest labeled superpixel. In the proposed superpixel-level classification scheme, each superpixel is regarded as a sample. This obviously greatly reduces the data volume to be classified. The experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed spectral–spatial classification method over several comparative state-of-the-art classification approaches, in terms of classification accuracy.

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Section: Introductionmentioning

confidence: 86%

Section: Appl Sci 2020 10 463 4 Of 15mentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

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A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

et al. 2020

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“…OA: overall accuracy, AA: average accuracy, κ: kappa coefficient. The classification results of the proposed SLC-DP method were visually and quantitatively compared with those provided by several state-of-the-art HSI classification approaches, i.e., edge-preserving filters (EPF) [59], image fusion and recursive filtering (IFRF) [60], logistic regression via variable splitting and augmented Lagrangian (LORSAL) [61], sparse multinomial logistic regression with a spatially adaptive total variation regularization (SMLR-S) [62], superpixel-based classification via multiple kernels (SCMK) [20], superpixel-wise PCA (SuperPCA) [63], and a support vector machine (SVM) based on superpixel and discontinuity-preserving relaxation (SVM-SD) [63]. The EPF, IFRF, SMLR-S, SCMK, SuperPCA, and SVM-SD algorithms are spectral-spatial classifiers, whereas the LORSAL method does not consider the spatial information of pixels in the classification.…”

Section: Evaluation Protocolmentioning

confidence: 99%

Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method

Zhao

Yan

2020

Remote Sensing

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Hyperspectral image (HSI) classification plays an important role in the automatic interpretation of the remotely sensed data. However, it is a non-trivial task to classify HSI accurately and rapidly due to its characteristics of having a large amount of data and massive noise points. To address this problem, in this work, a novel, semi-supervised, superpixel-level classification method for an HSI was proposed based on a graph and discrete potential (SSC-GDP). The key idea of the proposed scheme is the construction of the weighted connectivity graph and the division of the weighted graph. Based on the superpixel segmentation, a weighted connectivity graph is constructed usingthe weighted connection between a superpixel and its spatial neighbors. The generated graph is then divided into different communities/sub-graphs by using a discrete potential and the improved semi-supervised Wu–Huberman (ISWH) algorithm. Each community in the weighted connectivity graph represents a class in the HSI. The local connection strategy, together with the linear complexity of the ISWH algorithm, ensures the fast implementation of the suggested SSC-GDP method. To prove the effectiveness of the proposed spectral–spatial method, two public benchmarks, Indian Pines and Salinas, were utilized to test the performance of our proposal. The comparative test results confirmed that the proposed method was superior to several other state-of-the-art methods.

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“…Satisfactory classification results of plenty of existing spectral-spatial HSI classification methods have demonstrated the feasibility of this integration [11][12][13][14][15][16]. Using fixed-size window technique, the methods of Markov random field [17], guided filter [18], discontinuity preserving relaxation [19,20], and recursive filtering [21] successfully adopted spatial information to smooth the noisy pixels contained in the HSI. The use of these smoothing techniques in classification results in an effective reduction in the number of misclassified pixels.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Sparse Superpixel Graph

Zhao

Yan

2021

Remote Sensing

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Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.

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

Cited by 9 publications

References 62 publications

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

Novel Semi-Supervised Hyperspectral Image Classification Based on a Superpixel Graph and Discrete Potential Method

Hyperspectral Image Classification Based on Sparse Superpixel Graph

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