Virtual Dimensionality estimation by Double Subspace Projection for hyperspectral images

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

Virtual Dimensionality (VD) estimation is a key problem in feature/band selection and spectral mixture analysis of hyperspectral images. In this paper, a Double Subspace Projection (DSubP) based VD estimation algorithm is proposed. The pixel representation and image representation of a hyperspectral image are utilized to generate two subspaces according to the principal component analysis (PCA), respectively. When the dimensionality of these two subspaces exceeds VD of the hyperspectral image, both sub-space projections show the same reconstruction performance. Therefore, VD can be estimated by judging the difference of reconstruction performance between DSubP. Both synthetic and real hyperspectral experiments demonstrate that the performance of the proposed DSubP based VD estimation algorithm outperforms that of the HFC and NWHFC based VD estimation algorithms.

源语言英语
主期刊名2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010
234-237
页数4
DOI
出版状态已出版 - 2010
活动2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010 - Qingdao, 中国
期限: 28 8月 201031 8月 2010

出版系列

姓名2010 2nd IITA International Conference on Geoscience and Remote Sensing, IITA-GRS 2010
2

会议

会议2010 2nd IITA Conference on Geoscience and Remote Sensing, IITA-GRS 2010
国家/地区中国
Qingdao
时期28/08/1031/08/10

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