Superpixel-based composite kernel for hyperspectral image classification

Duan, Wuhui; Li, Shutao; Fang, Leyuan

doi:10.1109/igarss.2015.7326114

Cited by 12 publications

(6 citation statements)

References 8 publications

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“…Hence, various superpixel-based approaches are proposed to classify HSI. For instance, in [44], the composite kernel based on superpixel (SCK) method captures spatial features by calculating the mean of each superpixel. In [45], the superpixel-based multiple kernels (SCMK) method utilizes the spectral and spatial features between and within superpixels by three kernels.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Weighted Adjacent Superpixel-Based Composite Kernel for Hyperspectral Image Classification

Zhang

Chen

2021

Remote Sensing

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This paper presents a composite kernel method (MWASCK) based on multiscale weighted adjacent superpixels (ASs) to classify hyperspectral image (HSI). The MWASCK adequately exploits spatial-spectral features of weighted adjacent superpixels to guarantee that more accurate spectral features can be extracted. Firstly, we use a superpixel segmentation algorithm to divide HSI into multiple superpixels. Secondly, the similarities between each target superpixel and its ASs are calculated to construct the spatial features. Finally, a weighted AS-based composite kernel (WASCK) method for HSI classification is proposed. In order to avoid seeking for the optimal superpixel scale and fuse the multiscale spatial features, the MWASCK method uses multiscale weighted superpixel neighbor information. Experiments from two real HSIs indicate that superior performance of the WASCK and MWASCK methods compared with some popular classification methods.

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Section: Introductionmentioning

confidence: 99%

Multiscale Weighted Adjacent Superpixel-Based Composite Kernel for Hyperspectral Image Classification

Zhang

Chen

2021

Remote Sensing

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“…In [41], multiple kernels (MK) were applied to effectively utilize the spectral-spatial information of the superpixel. Compared with [39], [40], it not only utilized the spatial information within the superpixel but also utilized the spatial information among superpixels, resulting in higher classification accuracy. Due to the powerful feature extraction performance of multiscale information [42], [43], the classification of multiscale superpixels for HSI is proposed in [44], [45], which avoids the selection of the optimal superpixels.…”

Section: Introductionmentioning

confidence: 99%

“…This method uses the similarity of features between pixels to group pixels and uses a small number of superpixels to replace a large number of pixels to express image features, which greatly reduces the complexity of image postprocessing. In [39], [40], superpixels were regarded as a local neighborhood to obtain spatial information, thereby avoiding the choice of the best spatial neighborhood. In [41], multiple kernels (MK) were applied to effectively utilize the spectral-spatial information of the superpixel.…”

Section: Introductionmentioning

confidence: 99%

Local Binary Patterns and Superpixel-Based Multiple Kernels for Hyperspectral Image Classification

Huang

Yao

Wang

et al. 2020

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

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The superpixel-based multiple kernels model uses the average value of all pixels within superpixel as the spatial feature, which results in inaccurate extraction of edge pixels. To solve this problem, a local binary patterns and superpixel-based multiple kernels method is proposed for hyperspectral image (HSI) classification. First, the original HSI is segmented into multiple superpixels by using the entropy rate superpixel segmentation algorithm. On the HSI with superpixel index, the spectral kernel is second obtained by combining the spectral feature map with the radial basis kernel (RBF). By introducing local binary pattern (LBP) and weighted average filtering into RBF, the spatial kernels are obtained within and among superpixels. Finally, the combined kernel containing the abovementioned three kernels is inputted into the support vector machine classifier to generate a classification map. The experimental procedure in this article uses LBP to extract the information in superpixels, which effectively prevents the loss of edge features in superpixels. The experimental results show that the proposed method is superior to the state-of-the-art classifiers for HSI classification.

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“…Using image features and superpixels [35,36] to select homogeneous regions adaptively can overcome the shortcomings of the fixed square window. For example, the superpixel-based CK (SPCK) method [37] has been developed. However, there is no single kernel function which can cope with complicated HSIs.…”

Section: Introductionmentioning

confidence: 99%

“…SVM: Support vector marching-based classifier[46];(2) LRR: Low rank representation-based classifier[44];(3) SVMCK: Composite kernels and SVM-based method[32]; (4) SMLR_SPTV: Multinomial logistic regression and spatially adaptive total variation based method[26]; (5) SPCK: Superpixel based composite kernel and SVM classifier[37]; (6) SCMK: Superpixel, multiple kernels and SVM-based method[42]; (7) RMKL: Representative multiple kernel learning and SVM-based method[38]; (8) Sp_MKL_SVM: The proposed superpixel multiple kernel learning and SVM-based method; (9) Sp_MKL_LRR: The proposed method.…”

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confidence: 99%

Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

Zhan

Sun

et al. 2018

Remote Sensing

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High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to achieve good classification performance. In this paper, multiple kernel learning-based low rank representation at superpixel level (Sp_MKL_LRR) is proposed to improve the classification accuracy for hyperspectral images. Superpixels are generated first from the hyperspectral image to reduce noise effect and form homogeneous regions. An optimal superpixel kernel parameter is then selected by the kernel matrix using a multiple kernel learning framework. Finally, a kernel low rank representation is applied to classify the hyperspectral image. The proposed method offers two advantages. (1) The global correlation constraint is exploited by the low rank representation, while the local neighborhood information is extracted as the superpixel kernel adaptively learns the high-dimensional manifold features of the samples in each class; (2) It can meet the challenges of multiscale feature learning and adaptive parameter determination in the conventional kernel methods. Experimental results on several hyperspectral image datasets demonstrate that the proposed method outperforms several state-of-the-art classifiers tested in terms of overall accuracy, average accuracy, and kappa statistic.

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Superpixel-based composite kernel for hyperspectral image classification

Cited by 12 publications

References 8 publications

Multiscale Weighted Adjacent Superpixel-Based Composite Kernel for Hyperspectral Image Classification

Multiscale Weighted Adjacent Superpixel-Based Composite Kernel for Hyperspectral Image Classification

Local Binary Patterns and Superpixel-Based Multiple Kernels for Hyperspectral Image Classification

Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

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