利用分离性测度多类支持向量机进行高光谱遥感影像分类

提出了一种基于变换域离散度排序的高光谱图像快速压缩算法。该算法针对高光谱数据在Hadamard变换域的特性,自适应地选择有利的排列顺序,将变换域光谱矢量的各维度按照离散度进行重新排序,不仅使光谱矢量的大部分能量和差异集中在低维部分,而且把高信噪比的分量调整到低维空间,并据此构造出高效的码字排除不等式,最后结合LBG(Linde Bazo Gray)聚类算法,通过矢量量化快速完成高光谱图像的编码。在不同压缩比下进行实验,结果表明,本文提出的高光谱图像压缩算法能在保证良好的图像恢复质量的前提下,大幅度降低计算复杂度,实现快速压缩。

Hyperspectral Remote Sensing Data Analysis and Future Challenges

A fast compression algorithm for hyperspectral images based on dispersion sorting in transform domain is proposedConsidering the characteristics of hyperspectral data in the Hadamard domain, the proposed algorithm selects a favourable order adaptively and sorts the dimensions of spectral vectors by dispersion. Consequently, the energy and difference of the spectral vectors is concentrated on the lower dimensions and the dimensions of high signal to noise ratio are moved into low dimensional subspace. Then, efficient eliminating inequalities are constructed. When combinined with the LBG(Linde Bazo Gray) clustering algorithm, the proposed algorithm quickly completes the encoding of hyperspectral images via vector quantization. Experiments were conducted under different compression ratios The results show that, the compression algorithm for hyperspectral images as presented in this paper can reduce the computational complexity significantly when completing fast compression based on the precondition of good recovery quality.