Classification of hyperspectral images compressed through 3D-JPEG2000

Blanes I., Zabala A., Moré G., Pons X., Serra-Sagristà J. (2008) Classification of hyperspectral images compressed through 3D-JPEG2000. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5179 LNAI: 416-423.
Doi: 10.1007/978-3-540-85567-5-52


Classification of hyperspectral images is paramount to an increasing number of user applications. With the advent of more powerful technology, sensed images demand for larger requirements in computational and memory capabilities, which has led to devise compression techniques to alleviate the transmission and storage necessities. Classification of compressed images is addressed in this paper. Compression takes into account the spectral correlation of hyperspectral images together with more simple approaches. Experiments have been performed on a large hyperspectral CASI image with 72 bands. Both coding and classification results indicate that the performance of 3d-DWT is superior to the other two lossy coding approaches, providing consistent improvements of more than 10 dB for the coding process, and maintaining both the global accuracy and the percentage of classified area for the classification process. © 2008 Springer-Verlag Berlin Heidelberg.

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