Classification of hyperspectral and LIDAR data using extinction profiles with feature fusion

Mengmeng Zhang, Pedram Ghamisi, Wei Li*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

34 引用 (Scopus)

摘要

Hyperspectral images comprise hundreds of narrow contiguous wavelength bands which include wealth spectral information, and a great potential of Light detection and ranging (LIDAR) data lies in its benefits of height measurements, which can be used as complementary information for the classification of hyperspectral data. In this paper, a feature-fusion strategy of hyperspectral and LIDAR data is taken into account in order to develop a new classification framework for the accurate analysis of a surveyed area. The proposed approach employs extinction profiles (EPs) extracted with extinction filters computed on both hyperspectral and LIDAR images, leading to a fusion of the spectral, spatial, and elevation features. Experimental results obtained by using a real hyperspectral image along with LIDAR-derived digital surface model (DSM) collected over the University of Houston campus and its neighboring urban area demonstrate the effectiveness of the proposed framework.

源语言英语
页(从-至)957-966
页数10
期刊Remote Sensing Letters
8
10
DOI
出版状态已出版 - 3 10月 2017
已对外发布

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