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

Sensors

Huali Li¹  Dan Ma²  Junyi Huang³  Jing Qian⁴  Ping Liu⁴  Huijuan Chen⁴  Jun Liu⁴ 

[1] College of Electrical and Information Engineering, Hunan University, Hunan 410082, China;College of Resource and Environmental Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China;Department of Geography, Hong Kong Baptist University, Hong Kong, China;Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China;

关键词: similarity assessment;    spectrum absorption-reflection idex;    simplified curve pattern;    Douglas-Peucker algorithm;    hyperspectral remote sensing;   

DOI  :  10.3390/s16020152

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown