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High-resolution hyper-spectral image Classification with Parts-based Feature and Morphology Profile in Urban Area

Geo-Spatial Information Science 2010年第2期13卷 111-122页

作者： HUANG Yuancheng ZHANG Liangpei LI Pingxiang ZHONG Yanfei State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University 129Luoyu Road Wuhan 430079 China

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

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The recognition of ocean red tide with hyper-spectral-image based on EMD

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Chinese Journal of Oceanology and Limnology 2008年第2期26卷 137-141页

作者：赵文仓韦洪丽时长江姬光荣 College of Automation and Electronic Engineering Qingdao University of Science & Technology Shandong Entry-Exit Inspection and Quarantine Bureau Department of Electronic Engineering Ocean University of China

A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general pictre data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.

关键词： red tide recognition aerial remote sensing hyper-spectral image empirical mode decomposition (EMD) characteristic parameter

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