Title :
On the evaluation of synthetic hyperspectral imagery
Author :
Mendenhall, Michael J. ; Merényi, Erzsebet
Author_Institution :
Dept. of Electr. & Comput. Eng., Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA
Abstract :
In developing algorithms that exploit model-generated data, it is important to understand the realism of the data generated by that model. One way to address this issue is to exercise a well understood, yet diverse process, that will help draw out the strengths and weaknesses of the data generation system. We accomplish this by using a typical chain of processing steps on a synthetic hyperspectral image created by the Digital Imaging Remote Sensing Image Generation (DIRSIG) tool. The clustering, classification, and feature selection, which are part of this processing, are used to assess the realism of the data based on the performance compared to the similar analysis on real hyperspectral data.
Keywords :
geophysical signal processing; image classification; pattern clustering; remote sensing; Digital Imaging Remote Sensing Image Generation; data generation system; feature selection; image classification; image clustering; synthetic hyperspectral imagery; Data engineering; Digital images; Hyperspectral imaging; Hyperspectral sensors; Image analysis; Image generation; Performance analysis; Remote sensing; Shape; Vector quantization; Self-organizing map; learning vector quantization; relevance learning; synthetic hyperspectral imagery;
Conference_Titel :
Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2009. WHISPERS '09. First Workshop on
Conference_Location :
Grenoble
Print_ISBN :
978-1-4244-4686-5
Electronic_ISBN :
978-1-4244-4687-2
DOI :
10.1109/WHISPERS.2009.5289077
