# Multiband and Lossless Compression of Hyperspectral Images
## 1 Introduction  
Hyperspectral imaging instruments collect information by exploring the electromagnetic spectrum of a specific geographical area. In contrast to the human eye and traditional camera sensors, which can only perceive visible light (_i.e._, the wavelengths between 360 to 760 nanometers (nm)), spectral imaging techniques allow to cover a significant portion of wavelengths (_i.e._, the frequencies of ultraviolet and infrared rays). It is important to note that the spectrum is subdivided into different spectral bands. Therefore, hyperspectral images can be viewed as three-dimensional data (often referred as _datacubes_).  
For instance, the _Airborne Visible/Infrared Imaging Spectrometer_ (_AVIRIS_) [1] hyperspectral sensor (NASA Jet Propulsion Laboratory (JPL) [2]) measures from 380 to 2500 nm of the electromagnetic spectrum. In particular, the spectrum is subdivided into 224 spectral bands.  
From the analysis of hyperspectral data, it is possible to identify and/or classify materials, objects, _etc._ Such capabilities are related to the fact that some objects and materials have a unique signature (a sort of _fingerprint_) in the electromagnetic spectrum, therefore this fingerprint can be used for identification purposes.  
Hyperspectral data are widely used in real-life applications including agriculture, mineralogy, physics, surveillance, _etc._ For instance, in geological applications the capabilities of hyperspectral remote sensing are exploited to identify various types of minerals or to search for minerals and oil.  
One of the most important parameters to evaluate the precision of a sensor is the spectral resolution, which is the width between two adjacent bands. For instance, by considering the AVIRIS hyperspectral images, the spectral resolution is 10 nm. The spatial resolution is a relevant aspect too. Informally, the spatial resolution denotes how extensive is the geographical area mapped by the sensor into a pixel. It could be difficult to recognize materials and/or objects from a pixel, if a too wide area is mapped into it.  
Many hundreds of gigabytes can be produced every day by a single hyperspectral sensor. Therefore, it is necessary to compress these data, in order to transmit and to store them efficiently.  
Since such data are often used in delicate tasks and there are high costs involved in the acquisitions, lossless compression is generally required.  
This paper focuses on a novel technique for the lossless compression of hyperspectral images. The proposed algorithm is based on the predictive coding model and the proposed predictive structure uses a configurable multiband three-dimensional structure. It is possible to customize our predictor by individuating the number of the previous bands which will be used as references and the wideness of the prediction context. Through appropriate configurations of such parameters, the computational complexity and the memory usage can be optimized depending on the hardware available.  
Because of its high configurability, our algorithm is suitable for _\"on board\"_ implementations on hardware with limited capabilities, as for example on an airplane or on a satellite.  
The experimental results we have achieved are comparable and often better, with respect to other state of the art approaches. Our scheme provides a good trade-off between computational complexity/memory usage and compression performances.  
The rest of the paper is organized as follows: Section 2 briefly reviews previous work on lossless and lossy compression of hyperspectral images, Section 3 outlines the proposed lossless compression approach and Section 4 focuses on the description of experimental results. Finally, Section 5 highlights our conclusion and future work directions.