siat.cas.cn
20
六月
2017

基于高光谱反射-吸收指数和简化光谱曲线进行光谱相似性度量的方法

高光谱影像具有光谱信息丰富,波段带宽窄和波段个数多等特点。如何利用光谱特征的相似性来辨别地物的特征,在高光谱研究中是一个重要的问题。本文提出了一种基于光谱相似性度量方法,该方法同时考虑了光谱的形态和光谱的反射吸收指数。首先通过光谱的反射-吸收指数来对整个光谱特征响应曲线的形态进行约束。然后利用Douglas-Peucker算法简化光谱的形态。最后,将光谱之间通过特征点进行匹配,并利用特征点的位置计算光谱之间的相似性。AVIRIS和ROSIS高光谱数据用于测试方法的有效性。实验表明相对于传统的光谱匹配方法,新方法具有更好的精度,同时所需要的特征点也更少。


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.



Fig.1 The results of the IDP algorithm under SRAI-restriction with10 peak and 10 valley points.
(a) The original spectral curve, (b) 30 retained points, (c) 50 retained points, and (d) 70 retained points.



Fig.2 RGB color composite AVIRIS image. (a) True color image and (b) mean spectral curve of
three types of land cover



Fig.3 RGB color composite ROSIS image. (a) True color image and (b) mean spectral curve of three
types of land cover.

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