Hyperspectral image classification based on adaptive segmentation | |
Wu, Yinhua; Hu, Bingliang; Gao, Xiaohui; Wei, Ruyi | |
作者部门 | 光谱成像技术实验室 |
2018-11 | |
发表期刊 | Optik |
ISSN | 00304026 |
卷号 | 172页码:612-621 |
产权排序 | 1 |
摘要 | Object-based hyperspectral image classification (OBHIC) converts the basic unit from ‘pixel’ to ‘object’ by image segmentation, in order to take advantage of the spatial distribution law of geographical substances, as well as increase classification performances. However, it involves the problem of scale selection, i.e. the segmentation parameters are set manually by empirical values. In this paper, a novel OBHIC algorithm based on adaptive segmentation is proposed. Here, hyperspectral images (HSIs) are first segmented through a new segmentation scheme with constraint ability, and the thresholds for segmentation are calculated adaptively by utilizing training samples. And then K-nearest neighbor algorithm (KNN) is applied to classify the centers of each region after segmentation. In addition, based on the semisupervised idea, semi-known samples are obtained to further improve the classification performance. Experimental results are presented on two HSI datasets. For different HSIs, the adaptive thresholds calculated are consistent with empirical ones, and the developed classification algorithm has achieved good classification results, thus demonstrating strong robustness of the algorithm. For the HSI Indian Pines from AVIRIS sensor, the Overall Accuracy (OA) and kappa are 95.13% and 0.9444 respectively with 10% training samples, and for the HSI Pavia University from ROSIS sensor, the OA and kappa are 95.52% and 0.9416 respectively with 2% training samples. And good classification performance is still maintained for small number of training samples. © 2018 |
关键词 | Hyperspectral Classification Object-based Segmentation Adaptive |
DOI | 10.1016/j.ijleo.2018.07.058 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000445714700076 |
出版者 | Elsevier GmbH |
EI入藏号 | 20183005599681 |
EI主题词 | Classification (Of Information) ; Image Segmentation ; Independent Component Analysis ; Nearest Neighbor Search ; Sampling ; Spectroscopy |
EI分类号 | Electronics And Communication Engineering::Electronic Equipment, Radar, Radio And Television::Information & Communication Theory |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/30528 |
专题 | 光谱成像技术研究室 |
通讯作者 | Wu, Yinhua |
作者单位 | Key Laboratory of Spectral Imaging Technology, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China |
推荐引用方式 GB/T 7714 | Wu, Yinhua,Hu, Bingliang,Gao, Xiaohui,et al. Hyperspectral image classification based on adaptive segmentation[J]. Optik,2018,172:612-621. |
APA | Wu, Yinhua,Hu, Bingliang,Gao, Xiaohui,&Wei, Ruyi.(2018).Hyperspectral image classification based on adaptive segmentation.Optik,172,612-621. |
MLA | Wu, Yinhua,et al."Hyperspectral image classification based on adaptive segmentation".Optik 172(2018):612-621. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Hyperspectral image (2214KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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