Title of Presentation:  New Algorithms for the Classification and Compression of Hyperspectral Images

Primary (Corresponding) Author:  Tamal Bose

Organization of Primary Author:  Utah State University

Co-Authors: Bei Xie (Utah State University) and Erzsébet Merényi (Rice University)

Abstract: Classification and compression are common operations in image processing. Conventionally, compression and classification algorithms are independent of each other and performed sequentially. In this paper, a new algorithm is developed, where the two operations are combined in order to optimize some given classification metrics. In other words, the compression ratio is maximized under classification constraints. Compression is achieved using Adaptive Differential Pulse Code Modulation (ADPCM), which has an adaptive predictor. The predictor coefficients are updated in real-time by optimizing a cost function based on classification metrics. Optimization is done using a simple genetic algorithm. Computer simulations are performed on hyperspectral images. The results are promising and illustrate the performance of the algorithm under various constraints and compression schemes.