Hyperspectral imaging is of interest in a large number of remote sensing applications, such as geology and pollution monitoring, in order to detect and analyze surface and atmospheric composition. The processing of these images, called spectral analysis, allows for the identification of the specific mineralogical and agricultural elements which compose an image. We seek to understand how loss due to compression can affect the spectral analysis results, and then modify the compression algorithms to improve spectral analysis performance. More specifically, we suggest modifications to the 3D-SPIHT algorithm for improving the classification accuracy of hyperspectral images for two classification techniques: spectral angle mapper (SAM) and matched filtering (MF). Results of our modification show an improvement in the error rate as reported by the classification techniques we study, indicating an increase in the ability to analyze hyperspectral images which have been compressed.