Improvement Of Spectral Feature Extraction Algorithm And Application In Hyperspectral RS Oil Spill Images

Hyperspectral remote sensing technology draws the more attention of the public as a new remote sensing technology. The main reason is:Hyperspectral remote sensing technology has a higher spectral resolution and broader spectral imaging range. That makes it possible to detect surface features characteristic that can’t be detected in the ordinary remote sensing conditions. And it also makes spectral curve contact with geometry, so that people can intuitively feel the characteristics of the spectrum. Therefore, the surface features analysis also converted to the analysis of the similarities and differences in the spectral curve. The feature extraction of the spectral curve is the premise of spectroscopy. Therefore, it’s important to extract the characteristics of spectral curve efficiently and accurately.So this article in-depth studies on the spectral curve feature extraction algorithm. And the algorithm of curve tree which is in the pattern recognition is introduced to the feature extraction of the spectral curve. Then improve the curve tree algorithm by in-depth studying it, for making it more suitable for the characteristics of the spectral curve extraction.Firstly, spectral curves of six groups of surface features were collected by laboratory spectrometer. Binary encoding algorithm, curve tree algorithm and its improved algorithm were used to classify. And some new the curve were created by processing the certain curve. So a new set of curves were formed. Then Euclidean distances between them were calculated, after the characteristics of the curves were extracted. That proves that curve tree algorithm has invariance for the translation, rotation, stretching of curve.Finally, the curve tree algorithm and its improved algorithm were applied to the hyperspectral oil spill image. And the original hyperspectral images were inversed by the algorithm of log-residuals and internal average. Then the characteristics of the curve were extracted by the algorithms in this paper. Finally, the oil spills in the image were classified. It can be seen from result that the classification results of improved curve tree algorithm are better for the "pure" substances, and the results are bad for the situation of oil-water mixture.