光学精密工程2018，Vol.26Issue(2)：426-434,9.DOI:10.3788/OPE.20182602.0426
利用区域增长技术的自适应高光谱图像分类
Adaptive hyperspectral image classification using region-growing techniques
摘要
Abstract
Aiming at the problem of segmentation parameters setting in object-oriented hyperspectral classification method,an adaptive hyperspectral classification algorithm based on region-growing tech-niques 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 classifica-tion 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 thresh-olds 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 over-all 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.关键词
高光谱/分类/面向对象/区域增长/自适应Key words
hyperspectral/classification/object-oriented/region-growing/adaptive分类
信息技术与安全科学引用本文复制引用
吴银花,胡炳樑,高晓惠,周安安..利用区域增长技术的自适应高光谱图像分类[J].光学精密工程,2018,26(2):426-434,9.基金项目
国家自然科学基金资助项目(No.11327303) （No.11327303）
国家国际科技合作专项资助项目(No.2015DFA10140) （No.2015DFA10140）
国家自然科学基金资助项目(No.61405239) （No.61405239）
