ICA-Based Dimensionality Reduction and Compression of Hyperspectral Images
基于独立成分分析的高光谱图像数据降维及压缩

This paper proposes a dimensionality reduction and compression method of hyperspectral images based on Independent Component Analysis(ICA) for hyperspectral image analysis.At first hyperspectral features are extracted using ICA and dimensionality reduction is accomplished.Then,dimensionality reduction images are compressed by the predictive code and adaptive arithmetic code.The experimental results by using 220 bands and 64 bands hyperspectral data show that the method achieved higher compression ratio,more strong analysis capability and lower peak signal-to-noise ratio than dimensionality reduction based on Principal Components Analysis(PCA).