A Graph-Based Classification Method for Hyperspectral Images

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	A Graph-Based Classification Method for Hyperspectral Images
	Bai, Jun; Xiang, Shiming; Pan, Chunhong
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2013-02-01
卷号	51 期号:2 页码:803-817
文章类型	Article
摘要	The goal of this paper is to apply graph cut (GC) theory to the classification of hyperspectral remote sensing images. The task is formulated as a labeling problem on Markov random field (MRF) constructed on the image grid, and GC algorithm is employed to solve this task. In general, a large number of user interactive strikes are necessary to obtain satisfactory segmentation results. Due to the spatial variability of spectral signatures, however, hyperspectral remote sensing images often contain many tiny regions. Labeling all these tiny regions usually needs expensive human labor. To overcome this difficulty, a pixelwise fuzzy classification based on support vector machine (SVM) is first applied. As a result, only pixels with high probabilities are preserved as labeled ones. This generates a pseudouser strike map. This map is then employed for GC to evaluate the truthful likelihoods of class labels and propagate them to the MRF. To evaluate the robustness of our method, we have tested our method on both large and small training sets. Additionally, comparisons are made between the results of SVM, SVM with stacking neighboring vectors, SVM with morphological preprocessing, extraction and classification of homogeneous objects, and our method. Comparative experimental results demonstrate the validity of our method.
关键词	Classification Graph Cut (Gc) Hyperspectral Markov Random Field (Mrf) Support Vector Machine (Svm)
WOS标题词	Science & Technology ; Physical Sciences ; Technology
关键词[WOS]	SUPPORT VECTOR MACHINES ; MORPHOLOGICAL PROFILES ; URBAN AREAS ; SPATIAL CLASSIFICATION ; CUTS ; SEGMENTATION ; EXTRACTION ; KERNELS ; FIELDS ; MODEL
收录类别	SCI
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000314019500008
引用统计	被引频次：56[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3708
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Bai, Jun,Xiang, Shiming,Pan, Chunhong. A Graph-Based Classification Method for Hyperspectral Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2013,51(2):803-817.
APA	Bai, Jun,Xiang, Shiming,&Pan, Chunhong.(2013).A Graph-Based Classification Method for Hyperspectral Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,51(2),803-817.
MLA	Bai, Jun,et al."A Graph-Based Classification Method for Hyperspectral Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 51.2(2013):803-817.

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