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Remote Sens. 2016, 8(9), 757; doi:10.3390/rs8090757

Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis

1
Science Applications International Corporation (SAIC), 12010 Sunset Hills Rd., Reston, VA 20190, USA
2
Remote Sensing Center, Naval Postgraduate School, 833 Dyer Rd., Monterey, CA 93943, USA
3
Physics Department, Naval Postgraduate School, 833 Dyer Rd., Monterey, CA 93943, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Naser El-Sheimy, Zahra Lari, Adel Moussa, Gonzalo Pajares Martinsanz, Clement Atzberger and Prasad S. Thenkabail
Received: 14 May 2016 / Revised: 21 July 2016 / Accepted: 5 September 2016 / Published: 15 September 2016
(This article belongs to the Special Issue Multi-Sensor and Multi-Data Integration in Remote Sensing)
View Full-Text   |   Download PDF [21023 KB, uploaded 15 September 2016]   |  

Abstract

We developed a method to enhance compositional mapping from spectral remote sensing through the integration of visible to near infrared (VNIR, ~0.4–1 µm), shortwave infrared (SWIR, ~1–2.5 µm), and longwave infrared (LWIR, ~8–13 µm) data. Spectral information from the individual ranges was first analyzed independently and then the resulting compositional information in the form of image endmembers and apparent abundances was integrated using ISODATA cluster analysis. Independent VNIR, SWIR, and LWIR analyses of a study area near Mountain Pass, California identified image endmembers representing vegetation, manmade materials (e.g., metal, plastic), specific minerals (e.g., calcite, dolomite, hematite, muscovite, gypsum), and general lithology (e.g., sulfate-bearing, carbonate-bearing, and silica-rich units). Integration of these endmembers and their abundances produced a final full-range classification map incorporating much of the variation from all three spectral ranges. The integrated map and its 54 classes provide additional compositional information that is not evident in the VNIR, SWIR, or LWIR data alone, which allows for more complete and accurate compositional mapping. A supplemental examination of hyperspectral LWIR data and comparison with the multispectral LWIR data used in the integration illustrates its potential to further improve this approach. View Full-Text
Keywords: near infrared; shortwave infrared; longwave infrared; thermal infrared; multi-sensor; fusion; data integration; hyperspectral; classification; remote sensing near infrared; shortwave infrared; longwave infrared; thermal infrared; multi-sensor; fusion; data integration; hyperspectral; classification; remote sensing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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McDowell, M.L.; Kruse, F.A. Enhanced Compositional Mapping through Integrated Full-Range Spectral Analysis. Remote Sens. 2016, 8, 757.

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