Segmentation of Multispectral Data Simulated from Hyperspectral Imagery

Hyperspectral satellite imaging has been gaining enormous research attention due to the latest advancements in the sensor technology, and the amount of information it conveys. However, its efficient analysis, transfer, and storage are still big practical issues which need to be endured in on-board applications. In this paper, we verify if the simulated multispectral data (with significantly smaller number of bands) can be segmented with accuracy as high as obtained over its corresponding original hyperspectral imagery. Our experimental study, backed up with statistical tests, revealed that it is possible to dramatically decrease the transfer and storage requirements of the original hyperspectral data by simulating its multispectral counterpart without adversely affecting the classification performance of popular supervised learners.