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Sensors 2016, 16(2), 152; doi:10.3390/s16020152

Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing

1
College of Resource and Environmental Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
2
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
3
Department of Geography, Hong Kong Baptist University, Hong Kong, China
4
College of Electrical and Information Engineering, Hunan University, Hunan 410082, China
*
Author to whom correspondence should be addressed.
Academic Editor: Assefa M. Melesse
Received: 27 October 2015 / Revised: 20 January 2016 / Accepted: 21 January 2016 / Published: 26 January 2016
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [2207 KB, uploaded 26 January 2016]   |  

Abstract

Hyperspectral images possess properties such as rich spectral information, narrow bandwidth, and large numbers of bands. Finding effective methods to retrieve land features from an image by using similarity assessment indices with specific spectral characteristics is an important research question. This paper reports a novel hyperspectral image similarity assessment index based on spectral curve patterns and a reflection-absorption index. First, some spectral reflection-absorption features are extracted to restrict the subsequent curve simplification. Then, the improved Douglas-Peucker algorithm is employed to simplify all spectral curves without setting the thresholds. Finally, the simplified curves with the feature points are matched, and the similarities among the spectral curves are calculated using the matched points. The Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) hyperspectral image datasets are then selected to test the effect of the proposed index. The practical experiments indicate that the proposed index can achieve higher precision and fewer points than the traditional spectral information divergence and spectral angle match. View Full-Text
Keywords: similarity assessment; spectrum absorption-reflection idex; simplified curve pattern; Douglas-Peucker algorithm; hyperspectral remote sensing similarity assessment; spectrum absorption-reflection idex; simplified curve pattern; Douglas-Peucker algorithm; hyperspectral remote sensing
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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MDPI and ACS Style

Ma, D.; Liu, J.; Huang, J.; Li, H.; Liu, P.; Chen, H.; Qian, J. Spectral Similarity Assessment Based on a Spectrum Reflectance-Absorption Index and Simplified Curve Patterns for Hyperspectral Remote Sensing. Sensors 2016, 16, 152.

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