Hyperspectral image classification based on multiscale superpixels

doi:10.6046/zrzyyg.2020344

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(4297 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

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: whuwanghua@163.com;1195203650@qq.com

Issue Date: 24 September 2021

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Hua WANG
	Weiwei LI
	Zhigang LI
	Xueye CHEN
	Le SUN

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.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2020344 OR https://www.gtzyyg.com/EN/Y2021/V33/I3/63

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

Tab.1 Number of sample categories and sample sets(个)

Fig.6 Classification accuracycorresponding to the weight ϑ

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

[1]	Goetz A F H. Three decades of hyperspectral remote sensing of the Earth:A personal view[J]. Remote Sensing of Environment, 2009, 113(s1):5-16.
[2]	Uwe K, Andreas B, Udo S. Fusion trees for fast and accurate classification of hyperspectral data with ensembles of γ -divergence-based RBF networks[J]. Neural Computing and Applications, 2015, 26(2):253-262. doi: 10.1007/s00521-014-1634-9 url: http://link.springer.com/10.1007/s00521-014-1634-9
[3]	胥海威, 杨敏华, 韩瑞梅, 等. 用随机决策树群算法进行高光谱遥感影像分类[J]. 应用科学学报, 2011, 29(6):598-604.
[3]	Xu H W, Yang M H, Han R M, et al. Classification of hyperspectral remote sensing images using stochastic decision tree group algorithm[J]. Journal of Applied Sciences, 2011, 29(6):598-604.
[4]	Guo Y H, Yin X J, Zhao X C, et al. Hyperspectral image classification with SVM and guided filter[J]. Eurasip Journal on Wireless Communications and Networking, 2019(1):1-9.
[5]	廖建尚, 王立国. 面向空间自相关信息的高光谱图像分类方法[J]. 农业机械学报, 2018, 49(6):215-224.
[5]	Liao J S, Wang L G. Hyperspectral image classification method for spatial autocorrelation information[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(6):215-224.
[6]	Kavita B, Vijaya M. Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images[J]. Journal of the Indian Society of Remote Sensing, 2019, 47(11):1949-1958. doi: 10.1007/s12524-019-01041-2
[7]	朱瑞飞, 马经宇, 李竺强, 等. 多层感知卷积神经网络的国产多光谱影像分类[J]. 光学学报, 2020, 40(15):1-21.
[7]	Zhu R F, Ma J Y, Li Z Q, et al. Classification of domestic multispectral images based on multilayer perceptual convolutional neural network[J]. Acta Optica Sinica, 2020, 40(15):1-21.
[8]	Bera, Shrivastava. Analysis of various optimizers on deep convolutional neural network model in the application of hyperspectral remote sensing image classification[J]. International Journal of Remote Sensing, 2020, 41(7):2664-2683. doi: 10.1080/01431161.2019.1694725 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1694725
[9]	Mohamad J, Mauro D M, Pierre C. Hyperspectral image classification based on mathematical morphology and tensor decomposit-ion[J]. Mathematical Morphology-Theory and Applications, 2020, 4(1):1-30. doi: 10.1515/mathm-2020-0001 url: https://www.degruyter.com/document/doi/10.1515/mathm-2020-0001/html
[10]	Paulina, Koziol, Magda, et al. Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data[J]. Scientific Reports, 2018, 8(11):1444-1463. doi: 10.1038/s41598-018-19829-6 url: https://doi.org/10.1038/s41598-018-19829-6
[11]	杜培军, 夏俊士, 薛朝辉, 等. 高光谱遥感影像分类研究进展[J]. 遥感学报, 2020, 20(2):236-256.
[11]	Du P J, Xia J S, Xue Z H, et al. Review of hyperspectral remote sensing image classification[J]. Journal of Remote Sensing, 2020, 20(2):236-256.
[12]	Wang P Y, Zhu H Q, Chen N. Umms:Efficient superpixel segmentation driven by a mixture of spatially constrained uniform distribution[J]. Ieice Transactions on Information and Systems, 2020, 103(1):181-185.
[13]	Sanghyun P. Crab region extraction method from tidal flat images using superpixels[J]. Journal of Advanced Information Technology and Convergence, 2019, 9(2):29-39. doi: 10.14801/JAITC.2019.9.2.29 url: http://www.dbpia.co.kr/Journal/ArticleDetail/NODE09286089
[14]	Vitaliy K, Grzegorz M. Persistence-based resolution-independent meshes of superpixels[J]. Pattern Recognition Letters, 2020, 131(3):300-306. doi: 10.1016/j.patrec.2020.01.014 url: https://linkinghub.elsevier.com/retrieve/pii/S0167865520300131
[15]	Tang K W, Su Z X, Jiang W, et al. Superpixels for large dataset subspace clustering[J]. Neural Computing and Applications, 2019, 31(12):8727-8736. doi: 10.1007/s00521-018-3914-2 url: https://doi.org/10.1007/s00521-018-3914-2
[16]	Song D M, Tan X, Wang B, et al. Integration of super-pixel segmentation and deep-learning methods for evaluating earthquake-damaged buildings using single-phase remote sensing imagery[J]. International Journal of Remote Sensing, 2020, 41(3):1040-1066. doi: 10.1080/01431161.2019.1655175 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1655175
[17]	Amin B, Riaz M M, Ghafoor A. Automatic image matting of synthetic aperture radar target chips[J]. Radio Engineering, 2020, 29(1):228-234.
[18]	Yuan X H, Guo S J, Li C W, et al. Near infrared star centroid detection by area analysis of multi-scale super pixel saliency fusion map[J]. Tsinghua Science and Technology, 2019, 24(3):291-300. doi: 10.1109/TST.5971803 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5971803
[19]	陈允杰, 马辰阳, 孙乐, 等. 基于边缘修正的高光谱图像超像素空谱核分类方法[J]. 电子学报, 2019, 47(1):73-81.
[19]	Chen Y J, Ma C Y, Sun L. Edge-modified superpixel based spectral-spatial kernel method for hyperspectral image classification[J]. Acta Electronica Sinica, 2019, 47(1):73-81.
[20]	刘忠林, 吴一全, 邹宇. 多尺度红外超像素图像模型的小目标检测[J]. 中国图象图形学报, 2019, 24(12):2159-2173.
[20]	Liu Z L, Wu Y Q, Zou Y. Multiscale infrared superpixel-image model for small-target detection[J]. Journal of Image and Graphics, 2019, 24(12):2159-2173.
[21]	Kang X D, Duan P H, Li S T. Hyperspectral image visualization with edge-preserving filtering and principal component analysis[J]. Information Fusion, 2020, 57(5):130-143. doi: 10.1016/j.inffus.2019.12.003 url: https://linkinghub.elsevier.com/retrieve/pii/S1566253518303051
[22]	Shi C, Pun C M. Multiscale superpixel-based hyperspectral image classification using recurrent neural networks with stacked autoencoders[J]. IEEE Transactions on Multimedia, 2020, 22(2):487-501. doi: 10.1109/TMM.6046 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6046
[23]	Sun L, Ma C, Chen Y, et al. Adjacent superpixel-based multiscale spatial-spectral kernel for hyperspectral classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2019, 12(6):1909-1919.
[24]	刘纯, 洪亮, 陈杰, 等. 融合像素—多尺度区域特征的高分辨率遥感影像分类算法[J]. 遥感学报, 2015, 19(2):228-239.
[24]	Liu C, Hong L, Chen J, et al. Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image[J]. Journal of Remote Sensing, 2015, 19(2):228-239.
[25]	Ramzan M, Abid A, Khan H, et al. A review on state-of-the-art violence detection techniques[J]. IEEE Access, 2019, 7(9):107560-107575. doi: 10.1109/Access.6287639 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639
[26]	Chang C C, Huang H T. Automatic tuning of the RBF kernel parameter for batch-mode active learning algorithms:A scalable framework[J]. IEEE Transactions on Cybernetics, 2019, 49(12):4460-4472. doi: 10.1109/TCYB.6221036 url: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221036
[27]	尚坤, 李培军, 程涛. 基于合成核支持向量机的高光谱土地覆盖分类[J]. 北京大学学报:自然科学版, 2011, 47(1):109-114.
[27]	Shang K, Li P J, Cheng T. Land cover classification of hyperspectral data using composite kernel support vector machines[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2011, 47(1):109-114.
[28]	He J, Xu J L. Ultra-short-term wind speed forecasting based on support vector machine with combined kernel function and similar data[J]. Eurasip Journal on Wireless Communications and Networking, 2019(1):1-7.
[29]	Akbari D, Moradizadeh M, Akbari M. Spectral-spatial classification of hyperspectral imagery using neural network algorithm and hierarchical segmentation[J]. The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2019, XLII-2(W12):1-5. doi: 10.5194/isprs-archives-XLII-2-1-2018 url: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/1/2018/

[1]	ZHENG Zongsheng, LIU Haixia, WANG Zhenhua, LU Peng, SHEN Xukun, TANG Pengfei. Improved 3D-CNN-based method for surface feature classification using hyperspectral images[J]. Remote Sensing for Natural Resources, 2023, 35(2): 105-111.
[2]	ZHANG Guojian, LIU Shengzhen, SUN Yingjun, YU Kaijie, LIU Lina. Hyperspectral anomaly detection based on the weakly supervised robust autoencoder[J]. Remote Sensing for Natural Resources, 2023, 35(2): 167-175.
[3]	ZHANG Pengqiang, GAO Kuiliang, LIU Bing, TAN Xiong. Classification of hyperspectral images based on deep Transformer network combined with spatial-spectral information[J]. Remote Sensing for Natural Resources, 2022, 34(3): 27-32.
[4]	SUN Xiao, XU Linlin, WANG Xiaoyang, TIAN Ye, WANG Wei, ZHANG Zhongyue. Mineral identification from hyperspectral images based on the optimized K-P-Means unmixing method[J]. Remote Sensing for Natural Resources, 2022, 34(3): 43-49.
[5]	QU Haicheng, WAND Yaxuan, SHEN Lei. Hyperspectral super-resolution combining multi-receptive field features with spectral-spatial attention[J]. Remote Sensing for Natural Resources, 2022, 34(1): 43-52.
[6]	GAO Wenlong, ZHANG Shengwei, LIN Xi, LUO Meng, REN Zhaoyi. The remote sensing-based estimation and spatial-temporal dynamic analysis of SOM in coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(4): 235-242.
[7]	LIU Yongmei, FAN Hongjian, GE Xinghua, LIU Jianhong, WANG Lei. Estimation accuracy of fractional vegetation cover based on normalized difference vegetation index and UAV hyperspectral images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 11-17.
[8]	LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[9]	HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
[10]	ZHANG Rui, YOU Shucheng, DU Lei, LU Jing, HE Yun, HU Yong. High-resolution remote sensing image segmentation based on improved superpixel and marker watershed[J]. Remote Sensing for Land & Resources, 2021, 33(1): 86-95.
[11]	XIA Yan, HUANG Liang, CHEN Pengdi. Tobacco fine extraction from UAV image based on fuzzy-superpixel segmentation algorithm[J]. Remote Sensing for Land & Resources, 2021, 33(1): 115-122.
[12]	SUN Ke. Remote sensing image classification based on super pixel and peak density[J]. Remote Sensing for Land & Resources, 2020, 32(4): 41-45.
[13]	Benzuo YAO, Fang HE. Spatial and spectral feature hierarchical fusion for hyperspectral image feature extraction[J]. Remote Sensing for Land & Resources, 2019, 31(3): 59-64.
[14]	Xiaolu LIAO, Jia LIU, Xingxia ZHOU. Feature extraction and classification of hyperspectral image with ground-sky synchronization test[J]. Remote Sensing for Land & Resources, 2019, 31(3): 65-71.
[15]	Bingxiu YAO, Liang HUANG, Yansong XU. A high resolution remote sensing image segmentation method based on superpixel and graph theory[J]. Remote Sensing for Land & Resources, 2019, 31(3): 72-79.

Viewed

Full text

Abstract

Cited

Shared

Discussed