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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 63-71     DOI: 10.6046/zrzyyg.2020344
Hyperspectral image classification based on multiscale superpixels
WANG Hua1,2(), LI Weiwei2(), LI Zhigang2, CHEN Xueye1, SUN Le3
1. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China
2. Henan Key Laboratory of Food Safety Data Intelligence, Zhengzhou University of Light Industry, Zhengzhou 450002, China
3. Nanjing University of Information Science and Technology, School of Computer and Software, Nanjing 210044, China
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With the rapid development of remote sensing technology, the research on the classification methods of hyperspectral remote sensing images has received widespread attention. However, existing studies on the classification of hyperspectral remote sensing images conduct image segmentation using a single-scale superpixel method. As a result, the optimal superpixel number cannot be determined, image details are liable to be omitted, and a single kernel matrix cannot characterize multiple feature information, thus leading to a decrease in the classification precision. Therefore, this study proposes to perform multiscale superpixel segmentation of the first principal component of hyperspectral images. Then it conducts hyperspectral image classification using the composite kernel obtained by coupling the multiscale spatial-spectral kernel with the original spatial-spectral kernel according to weights. Finally, it tests and analyzes the proposed method using the hyperspectral images of the National Mall in Washington, D.C. as experimental data. The test results show that the effective classification precision of this method is 6.93% higher than that of the compared methods. As proved by the results, this method can be used to effectively solve the problems such as the lack of self-adaption of image spectra and incomplete spectrum information acquired, thus significantly improving the classification accuracy of hyperspectral images.

Keywords RBF kernel function      multiscale      superpixel      composite kernel SVM      hyperspectral image     
ZTFLH:  TP75  
Corresponding Authors: LI Weiwei     E-mail:;
Issue Date: 24 September 2021
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Weiwei LI
Zhigang LI
Xueye CHEN
Cite this article:   
Hua WANG,Weiwei LI,Zhigang LI, et al. Hyperspectral image classification based on multiscale superpixels[J]. Remote Sensing for Natural Resources, 2021, 33(3): 63-71.
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Fig.1  Overall flowchart
Fig.2  Superpixel segmentation image
Fig.3  Multi-scale filtering spatial spectrum kernel acquisition process
Fig.4  Single-scale superpixel spatial spectrum nuclear acquisition process
Fig.5  Original space spectrum nuclear acquisition process
类别 训练集数量 测试集数量
住宅 500 3 365
公路 60 356
街道 50 89
草地 60 358
林地 60 345
水域 60 389
阴影 30 39
Tab.1  Number of sample categories and sample sets(个)
Fig.6  Classification accuracycorresponding to the weight ϑ
类别 Ms-SSSK
住宅 99.28 98.57 98.30 96.67
公路 98.32 94.36 92.68 87.55
街道 98.70 98.01 96.70 91.60
草地 97.35 95.25 92.12 89.16
林地 97.60 96.88 96.37 93.65
水域 98.65 96.31 98.71 96.87
阴影 98.16 96.98 96.60 87.73
总体精度/% 98.53 96.49 95.33 91.60
Kappa 0.960 1 0.948 9 0.921 6 0.903 1
Tab.2  Comparison of classification results
Fig.7  Classification result of test set
Fig.8  Overall image comparison
Fig.9  Local image comparison
Fig.10  Relative error results of each model
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