文章基本信息

标题：A Graph-based Technique for the Spectral-spatial Hyperspectral Images Classification
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作者：F. Poorahangaryan ; H. Beheshti ; S.A. Edalatpanah 等
期刊名称：International Transaction of Electrical and Computer Engineers System
印刷版ISSN：2373-1273
电子版ISSN：2373-1281
出版年度：2017
卷号：4
期号：1
页码：1-7
DOI：10.12691/iteces-4-1-1
出版社：Science and Education Publishing
摘要：Minimum Spanning Forest (MSF) is a graph-based technique used for segmenting and classification of images. In this article, a new method based on MSF is introduced that can be used to supervised classification of hyperspectral images. For a given hyperspectral image, a pixel-based classification, such as Support Vector Machine (SVM) or Maximum Likelihood (ML) is performed. On the other hand, dimensionality reduction is carried out by Principal Components Analysis (PCA) and the first eight components are considered as the reference data. The most reliable pixels, which are obtained from the result of pixel-based classifiers, are used as markers in the construction of MSF. In the next stage, three MSF’s are created after considering three distinct criteria of similarity (dissimilarity). Ultimately, using the majority voting rule, the obtained classification maps are combined and the final classification map is formed. The simulation results presented on an AVRIS image of the vegetation area indicate that the proposed technique enhanced classification accuracy and provides an accurate classification map.
关键词：hyperspectral images; spectral-spatial classification; minimum spanning forest (MSF); segmentation