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Adaptive hyperspectral image classification using region-growing techniques
Alternative Title利用区域增长技术的自适应高光谱图像分类
Wu Yinhua; Hu Bingliang; Gao Xiaohui; Zhou Anan; WU Yin-hua (yinhuawoo@163.com)
Department光谱成像技术实验室
2018
Source PublicationOptics and Precision Engineering
ISSN1004-924X
Volume26Issue:2Pages:426-434
Contribution Rank1
Abstract

Aiming at the problem of segmentation parameters setting in object-oriented hyperspectral classification method,an adaptive hyperspectral classification algorithm based on region-growing techniques was proposed in this paper.Firstly,a constrained region-growing method was proposed,which used the spatial information of the training samples to provide effective constraints,thus reducing the error propagation rate of the region markers in the region-growing process,and improving classification performance.Secondly,an adaptive threshold calculation method was proposed.By analyzing the distribution law of the spectrum of the training samples,the reasonable threshold for region division was calculated adaptively to replace the empirical threshold,so that the robustness of the algorithm was improved.Finally,the K-nearest neighbor algorithm (KNN)was used to classify the centers of each region after division.Experimental results show that:For different images,the adaptive thresholds calculated by the method are consistent with the empirical values,and the classification effect of the proposed algorithm is better than other algorithms.For hyperspectral data Indian Pines from AVIRIS sensor,the overall classification accuracy and kappa are 92.94% and 0.919 5 respectively with 10%training samples,and for hyperspectral data Pavia University from ROSIS sensor,the overall classification accuracy and kappa are 95.78%and 0.944 0 respectively with 5%training samples. The proposed algorithm not only enhances the robustness of the algorithm,but also improves the classification performance effectively,and has strong practicability in hyperspectral applications.

KeywordHyperspectral Classification Object-oriented Region-growing Adaptive
Indexed ByEI ; CSCD
Language中文
WOS Research AreaRemote Sensing (Provided By Clarivate Analytics)
CSCD IDCSCD:6191019
EI Accession Number20182705389856
Citation statistics
Cited Times:4[CSCD]   [CSCD Record]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30165
Collection光谱成像技术实验室
Corresponding AuthorWU Yin-hua (yinhuawoo@163.com)
AffiliationXi'an Institute of Optics and Precision Mechanics of CAS, Xi'an, 710119, China
Recommended Citation
GB/T 7714
Wu Yinhua,Hu Bingliang,Gao Xiaohui,et al. Adaptive hyperspectral image classification using region-growing techniques[J]. Optics and Precision Engineering,2018,26(2):426-434.
APA Wu Yinhua,Hu Bingliang,Gao Xiaohui,Zhou Anan,&WU Yin-hua .(2018).Adaptive hyperspectral image classification using region-growing techniques.Optics and Precision Engineering,26(2),426-434.
MLA Wu Yinhua,et al."Adaptive hyperspectral image classification using region-growing techniques".Optics and Precision Engineering 26.2(2018):426-434.
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