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.