Though widely used for spectral discrimination of materials, the spectral angle mapper (SAM) metrics exhibits some limitations, due to its lack of monotonicity as the number of components, i.e., spectral bands, increases. This paper proposes an outcome of the band add-on (BAO) decomposition of SAM, known as as BAO-SAM, for assessing compressed hyperspectral data. Since the material discrimination capability of BAO-SAM is superior to that of SAM, the underlying idea is that if the BAO-SAM between compressed and uncompressed data is kept low, the discrimination capability of compressed data will be favored. Experimental results on AVIRIS data show that BAO-SAM is capable of characterizing the spectral
distortion better than SAM does. Furthermore, the possibility of
developing a BAO-SAM bounded compression method is investigated. Such a method is likely to be useful for a variety of applications concerning hyperspectral image analysis.