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Proceedings Paper

Relative utility of HYDICE and multispectral data for object detection, identification, and abundance estimation
Author(s): Sylvia S. Shen
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Paper Abstract

Land cover and land use classification, and area estimation of spatially resolved objects have been successfully derived from remotely sensed imagery such as Landsat multispectral data for over 20 years. For subpixel objects, multispectral instruments may not provide sufficient distinct spectral information to reliably decompose a pixel into substances which make up that pixel. Hyperspectral imaging instruments with their large number of registered spectral bands may provide a capability to produce improved detection and classification of spatially resolved objects. These instruments are also designed to allow more reliable spectral decomposition of a pixel into pure substances, therefore permitting subpixel target material detection and abundance estimation. This paper describes an on-going study effort whose objective is to demonstrate the unique attributes and added contributions of hyperspectral data in comparison with simulated multispectral data sets for detection, discrimination, material identification, functional identification and abundance estimation problems. Methodologies used to perform these nonliteral exploitation tasks are described in this paper. Also presented in this paper are results obtained from applying these exploitation techniques to the Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor data and the simulated multispectral data sets for small to subpixel targets against a desert background. Performance comparison is made in terms of detection success rate, false alarms, and the number of correctly identified targets. These performance measures are also presented in this paper. This study is currently being extended to data collected by the HYDICE sensor in other types of background environment such as the forests to allow an assessment of the sensitivity of complex background on the relative utility of hyperspectral and multispectral data for key exploitation tasks.

Paper Details

Date Published: 6 November 1996
PDF: 12 pages
Proc. SPIE 2821, Hyperspectral Remote Sensing and Applications, (6 November 1996); doi: 10.1117/12.257174
Show Author Affiliations
Sylvia S. Shen, The Aerospace Corp. (United States)

Published in SPIE Proceedings Vol. 2821:
Hyperspectral Remote Sensing and Applications
Sylvia S. Shen, Editor(s)

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