由于遥感超谱图象谱分辨高的提高，如今已可以获得比多光谱图象更丰富的信息，并使得许多原先用多光谱信息不能解决的问题现在可以得到解决，它的问世是遥感技术应用的一个重大飞跃。另外，分类和压缩是目前国际上对超谱图象研究非常活跃的两个相对彼此独立、又相互联系的专题，因为压缩可以看作是给不同的子块分配不同的码字而实现的一种分类；反过来，分类也可以看作是一种提取感兴趣的地物信息的压缩。两者的差别主要在于评价最后处理结果的出发点不同，压缩一般侧重于恢复图象的平均误差，而分类则侧重于分类结果的错分概率。由于两者具有内在的相互联系，因此在实现算法上有许多相似之处，为了使人们对其发展的现状有所了解，因此对目前超谱图象分类和压缩广泛应用的方法进行了全面的综述，并对二者在应用中的相同之处和不同点作了比较分析，在此基础上，结合具体实例分别介绍了进行超谱图象分类和压缩的过程，并进行了计算机模拟仿真，最后给出了相应的结论和进一步研究的建议。

Remote sensing hyperspectral images provide more information than multispectral images in the sense that the spectral resolution of the former is much higher than that of the latter. They can solve the problems that the multispectral images can not. Their invention is a leap in the techniques of remote sensing for applications. There are two key subjects-classification and compression—for the researches of hyperspectral images now, which are both independent and dependent. Compression can be viewed as a kind of claasification realized by allocating different code words to different sub-blocks;on the contrary classification also can be considered as a type of compression extracting interesting object information. The main difference between them lies in the different standpoints eveluating the last results. Compression emphasizes the mean error of the reconstructed image, and classification emphasizes the misclassified probability of the images. Because of their inner relation, there are many similar realization algorithms. This paper first summarizes the methods of hyperspectral image classification and compression. Then the similar characteristics and differences for both are compared. Following two schemes for hyperspectral image classification and compression are introduced, and the computer simulations are carried out. Finally, the conclusions are given and the further research techniques are suggested.