文章基本信息

标题：High-resolution hyper-spectral image classification with parts-based feature and morphology profile in urban area
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作者：Yuancheng Huang ; Liangpei Zhang ; Pingxiang Li 等
期刊名称：Geo-spatial Information Science
印刷版ISSN：1009-5020
电子版ISSN：1993-5153
出版年度：2010
卷号：13
期号：2
页码：111-122
DOI：10.1007/s11806-010-0004-8
出版社：Taylor and Francis Ltd
摘要：High-resolution hyper-spectral image (HHR) provides both detailed structural and spectral information for urban study. However, due to the inherent correlation between spectral bands and within-class variability in the data, the data processing of HHR is a challenging work. In this paper, based on spectral mixture analysis theory, a new stack of parts description features were extracted, and then incorporated with a stack of morphology based spatial features. Partially supervised constrained energy minimization (CEM) and unsupervised nonnegative matrix factorization (NMF) were used to extract the part-features. The joint features were then integrated by SVM classifier. The advantages of this method are the representation of physical composition of the urban area by the parts-features and the show of multi-scale structure information by morphology profiles. Experiments with an airborne hyper-spectral data flightline over the Washington DC Mall were performed, and the performance of the proposed algorithm was evaluated in comparison with well-known nonparametric weighted feature extraction (NWFE) and feature selection method. The results shown that the proposed features-joint scheme consistently outperforms the traditional methods, and so can provide an effective option for processing HHR data in urban area.
关键词：parts-features; CEM; NMF; morphology profiles; hyper-spectral image; urban classification