高光谱图像的无损压缩

Raffaele Pizzolante
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引用次数: 7

摘要

本文回顾了文献[2]中提出的面向光谱的最小二乘(SLSQ)算法:一种高效、低复杂度的高光谱图像无损压缩算法。随后,我们考虑了两个重要的度量:Pearson’s Correlation和Bhattacharyya distance,并描述了基于该距离的波段排序方法。最后,我们报告了基于java的SLSQ在NASA喷气推进实验室机载可见/红外成像光谱仪(AVIRIS)获取的数据立方上实现的实验结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lossless Compression of Hyperspectral Imagery
In this paper we review the Spectral oriented Least SQuares (SLSQ) algorithm : an efficient and low complexity algorithm for Hyper spectral Image loss less compression, presented in [2]. Subsequently, we consider two important measures : Pearson's Correlation and Bhattacharyya distance and describe a band ordering approach based on this distances. Finally, we report experimental results achieved with a Java-based implementation of SLSQ on data cubes acquired by NASA JPL's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS).
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