Hyperspectral remote sensing produces a huge amount of three-dimensional digital data: the hyperspectral images. Hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. They are considered a useful tool in different real-life applications. In this paper we propose a novel approach for the efficient lossless compression of hyperspectral images, which is based on a predictive coding model. Our approach relies on a three-dimensional predictive structure that uses, one or more, previous bands as references to exploit the redundancies among the third dimension. The proposed technique uses limited resources in terms of CPU and memory usage. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.
Lossless, Multiband, on Board, Compression of Hyperspectral Images
CARPENTIERI, Bruno;PIZZOLANTE, RAFFAELE
2015
Abstract
Hyperspectral remote sensing produces a huge amount of three-dimensional digital data: the hyperspectral images. Hyperspectral images are used to recognize objects and to classify materials on the surface of the earth. They are considered a useful tool in different real-life applications. In this paper we propose a novel approach for the efficient lossless compression of hyperspectral images, which is based on a predictive coding model. Our approach relies on a three-dimensional predictive structure that uses, one or more, previous bands as references to exploit the redundancies among the third dimension. The proposed technique uses limited resources in terms of CPU and memory usage. The achieved results are comparable, and often better, with respect to the other state-of-art lossless compression techniques for hyperspectral images.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
