基于光谱线性分解的高光谱图像高效压缩

Efficient compression of hyperspectral images based on spectral linear decomposition

摘要:

高光谱图像有效压缩对于实现实时传输具有重要意义。本文将光谱线性分解应用于高光谱图像的高效压缩中，根据高光谱图像的线性混合模型，将高光谱数据分解为端元与丰度的乘积，编码端对端元与丰度进行必要的数据处理，然后分别进行JPEG-LS无损压缩，形成输出码流数据。解码端利用最终解码后的端元与丰度相乘来重建原始图像，探讨了量化步长对率失真性能的影响。仿真实验结果表明，该方法能够取得一定的压缩性能。

Abstract:

Effective compression of hyperspectral images is of great significance for real-time transmission. In this paper, spectral linear decomposition is introduced into efficient compression of hyperspectral image. According to the Linear Mixed Model(LMM), the hyperspectral data is decomposed into the product of endmember and abundance. At the encoder, the necessary data processing is performed on the endmembers and abundance, followed by JPEG-LS lossless compression. At the decoder, the original hyperspectral image is reconstructed by multiplying the final decoded endmembers and abundance, moreover, the effect of the quantization step on the rate-distortion performance is discussed. Experimental results show that the proposed method can achieve certain compression performance.