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High-resolution Hyper-spectral Image Classification with Parts-based Feature and Morphology Profile in Urban Area

ISSN号：1009-5020
期刊名称：《地球空间信息科学学报：英文版》
时间：0
分类：TP751[自动化与计算机技术—控制科学与工程;自动化与计算机技术—检测技术与自动化装置]
作者机构：[1]State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129Luoyu Road, Wuhan 430079, China
相关基金：Supported by the Major State Basic Research Development Program （973 Program） of China （No.2009CB723905）： lhe National High Technology Research and Development Program （863 Program） of China （No.2009AA12Zl14）; the National Natural Science Foundation of China （Nos. 40930532,40901213,40771139）.

作者： HUANG Yuancheng ZHANG Liangpei LI Pingxiang ZHONG Yanfei[1]

关键词：部分特征, CEM, NMF, 形态学侧面, 超图象, 城市的分类, parts-features, CEM, NMF, morphology profiles, hyper-spectral image, urban classification

中文摘要：

高分辨率的超图象(HHR ) 提供详细说明的两个为城市的学习的结构、光谱的信息。然而，由于固有的关联在之间光谱在数据的乐队和在内班可变性， HHR 处理的数据是一个挑战性的工作。在这糊，基于光谱混合分析理论，部分描述特征的新栈与形态学的栈被提取，然后合并基于的空间特征。部分监督的抑制精力最小化(CEM ) 和无指导的 nonnegative 矩阵因式分解(NMF ) 被用来提取部分特征。联合特征当时由 SVM 分类器是综合的。这个方法的优点是由部分特征和由形态学侧面的多尺度的结构信息的表演的城市的区域的物理作文的表示。有在华盛顿特区广场上的在空中的超数据 flightline 的实验被执行，并且建议算法的性能与著名 nonparametric 比较被评估加权的特征抽取(NWFE ) 和特征选择方法。显示出的结果建议特征关节计划一致地超过传统的方法，并且能那么为在城市的区域处理 HHR 数据提供一种有效选择。

英文摘要：

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 ex- tracted, and then incorporated with a stack of morphology based spatial features. Partially supervised constrained energy minimiza- tion （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.

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