NDLI: Vegetation classification using hyperspectral and multi-angular remote sensing data

Content Provider	IEEE Xplore Digital Library
Author	Baoxin Hu Freemantle, J. Miller, J. Smith, A.
Copyright Year	2007
Description	Author affiliation: York Univ., Toronto (Baoxin Hu; Freemantle, J.; Miller, J.)
Abstract	In this study, vegetation cover type classification was investigated using CHRIS data over agricultural scenes acquired across the 2004 growing season. Spectral indices sensitive to crop chlorophyll content and leaf area index were first calculated from CHRIS nadir data in May, June and July. The seasonality of these indices was analyzed and employed to identify crop types in the study area. To further improve the classification accuracy, the angular signatures of the vegetation canopies were derived from the nadir and off- nadir data in the red and near-infrared band using the kernel-driven Ross_Thick and Li-Sparse model. The coefficients of the kernel-based model were then used for crop type classification, together with the spectral indices derived from nadir data. Preliminary results show that the additional angular information can slightly improve the classification accuracy.
Starting Page	1749
Ending Page	1750
File Size	1415570
Page Count	2
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4423157
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hyperspectral sensors Hyperspectral imaging Remote sensing Crops Vegetation mapping Springs Scattering Spatial resolution Reflectivity Geoscience vegetation classification Hyperspectral Multi-angular
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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