Fast Linear Prediction Hyperspectral Image Visualization Algorithm

Abstract

Abstract How to generate a color image with as much information as possible from hyperspectral images on a trichromatic display is a subject of great concern. In a sense, effective bands containing important information can be extracted from hyperspectral data for image visualization through the method of band selection. In this paper, two improved methods are proposed for the linear prediction (LP) band selection algorithm. According to the information entropy, the band with the greatest difference in information domain is selected as initial band.Then, the max pooling method is used to select the pixel which can better reflect the image features. Finally, the band similarity is used to select the most distinctive band for hyperspectral image visualization. The experimental results are measured by the metrics such as feature separability, standard deviation, operation efficiency and image displaying detail.The final experimental results show that fast linear prediction (LP) algorithm is an effective method for hyperspectral image visualization.