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Proceedings Paper

Using hyperspectral remote sensing for land cover classification
Author(s): Wendy W. Zhang; Shobha Sriharan
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Paper Abstract

This project used hyperspectral data set to classify land cover using remote sensing techniques. Many different earth-sensing satellites, with diverse sensors mounted on sophisticated platforms, are currently in earth orbit. These sensors are designed to cover a wide range of the electromagnetic spectrum and are generating enormous amounts of data that must be processed, stored, and made available to the user community. The Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) collects data in 224 bands that are approximately 9.6 nm wide in contiguous bands between 0.40 and 2.45 mm. Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-IR, and thermal IR portions of the spectrum. The unsupervised image classification procedure automatically categorizes the pixels in an image into land cover classes or themes. Experiments on using hyperspectral remote sensing for land cover classification were conducted during the 2003 and 2004 NASA Summer Faculty Fellowship Program at Stennis Space Center. Research Systems Inc.'s (RSI) ENVI software package was used in this application framework. In this application, emphasis was placed on: (1) Spectrally oriented classification procedures for land cover mapping, particularly, the supervised surface classification using AVIRIS data; and (2) Identifying data endmembers.

Paper Details

Date Published: 20 January 2005
PDF: 10 pages
Proc. SPIE 5655, Multispectral and Hyperspectral Remote Sensing Instruments and Applications II, (20 January 2005); doi: 10.1117/12.578104
Show Author Affiliations
Wendy W. Zhang, Southern Univ. at New Orleans (United States)
Shobha Sriharan, Virginia State Univ. (United States)


Published in SPIE Proceedings Vol. 5655:
Multispectral and Hyperspectral Remote Sensing Instruments and Applications II
Allen M. Larar; Makoto Suzuki; Qingxi Tong, Editor(s)

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