Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification

Yu, Haoyang; Gao, Lianru; Liao, Wenzhi; Zhang, Bing; Pižurica, Aleksandra; Philips, Wilfried

doi:10.1109/lgrs.2017.2755061

Cited by 76 publications

(40 citation statements)

References 13 publications

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“…The classification results for the different number of dilation factors are shown in Figure 18. In this experiment, seven kinds of combination for the dilation factors were considered including: (1), (1,2), (1,2,3), (1,2,3,4), (1,2,3,4,5), (1,2,3,4,5,6) and (1,2,3,4,5,6,7), and 30 samples per class were selected as training samples. With the increasing of number dilation factors, the classification accuracy also improves.…”

Section: Evaluating the Multiple Receptive Field Fusion Blockmentioning

confidence: 99%

“…Multiscale spatial-spectral classification methods can be categorized into two groups: multiscale superpixel segmentation [6][7][8][9] and multiscale image cubes [10][11][12][13][14][15]. Numerous methods have been developed to determine the optimal scale in multiscale superpixel segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous methods have been developed to determine the optimal scale in multiscale superpixel segmentation. Yu et al [6] proposed a multiscale superpixel-level support vector machine (SVM) classification method to exploit the spatial information within different superpixel scales. Zhang et al [7] applied a multiscale superpixel-based sparse representation algorithm to obtain spatial structure information for different segmentation scales and generated a classification map using a sparse representation classifier.…”

Section: Introductionmentioning

confidence: 99%

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Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

et al. 2019

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Jointly using spatial and spectral information has been widely applied to hyperspectral image (HSI) classification. Especially, convolutional neural networks (CNN) have gained attention in recent years due to their detailed representation of features. However, most of CNN-based HSI classification methods mainly use patches as input classifier. This limits the range of use for spatial neighbor information and reduces processing efficiency in training and testing. To overcome this problem, we propose an image-based classification framework that is efficient and straightforward. Based on this framework, we propose a multiscale spatial-spectral CNN for HSIs (HyMSCN) to integrate both multiple receptive fields fused features and multiscale spatial features at different levels. The fused features are exploited using a lightweight block called the multiple receptive field feature block (MRFF), which contains various types of dilation convolution. By fusing multiple receptive field features and multiscale spatial features, the HyMSCN has comprehensive feature representation for classification. Experimental results from three real hyperspectral images prove the efficiency of the proposed framework. The proposed method also achieves superior performance for HSI classification.

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Section: Evaluating the Multiple Receptive Field Fusion Blockmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

et al. 2019

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“…To compare the proposed classification scheme with other state-of-the-art methods, it is important to select the same dataset, the same number of labeled samples and those classification approaches related to superpixels or spatial structure. Based on this consideration, we compare the proposal with different HSI classification methods, including SVM with the Extended Morphological Profile (EMP) and superpixels (EMP-SP-SVM) [56], multi-scale superpixel (MSP) and subspace-based SVM (MSP-SVMsub) [57], superpixel-based discriminative sparse model (SBDSM) [52], superpixel and extreme learning machines (SP-ELM) [55], superpixel-based spatial pyramid representation (SP-SPR) [61], multiple kernel learning-based low rank representation at superpixel level (SP-MKL-LRR) [48] segmented stacked autoencoder (S-SAE) [18] and spectral-spatial correlation segmentation-based classifier (SoCRATE) [21], SuperPCA [54]. All of these approaches try to use superpixels or spatial structure to improve the accuracy of classification results.…”

Section: Comparative Testmentioning

confidence: 99%

“…Over the past decade, superpixel segmentation methods have also been extended to HSI classification [42][43][44][45][46][47][48][49][50], aiming at making full use of spectral information and spatial structure in hyperspectral data. By the combination of different segmentation techniques with various classification methods, a number of approaches for HSI classification have been developed, such as ER with sparse representation [42,[51][52][53], SVM [54] or extreme learning machines [55], SLIC with multi-morphological method [56], SVM [57] or convolutional neural network [58] and so on. These HSI classification methods based on superpixel segmentation display good performance in experiments.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Hyperspectral image (HSI) classification is one of the most active topics in remote sensing. However, it is still a nontrivial task to classify the hyperspectral data accurately, since HSI always suffers from a large number of noise pixels, the complexity of the spatial structure of objects and the spectral similarity between different objects. In this study, an effective classification scheme for hyperspectral image based on superpixel and discontinuity preserving relaxation (DPR) is proposed to discriminate land covers of interest. A novel technique for measuring the similarity of a pair of pixels in HSI is suggested to improve the simple linear iterative clustering (SLIC) algorithm. Unlike the existing application of SLIC technique to HSI, the improved SLIC algorithm can be directly used to segment HSI into superpixels without using principal component analysis in advance, and is free of parameters. Furthermore, the proposed three-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Compared with the existing two-step classification framework, the use of DPR technology in preprocessing significantly improves the classification accuracy. The effectiveness of the proposed method is verified on three public real hyperspectral datasets. The comparison results of several competitive methods show the superiority of this scheme.

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Sparse Representation‐Based Hyperspectral Image Classification

et al. 2022

Advances in Hyperspectral Image Processing Techniques

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Multiscale Superpixel-Level Subspace-Based Support Vector Machines for Hyperspectral Image Classification

Cited by 76 publications

References 13 publications

Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

Multiscale Spatial-Spectral Convolutional Network with Image-Based Framework for Hyperspectral Imagery Classification

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

Sparse Representation‐Based Hyperspectral Image Classification

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