Advancements in image sensors and signal processing have led to the successful development of lightweight hyperspectral
imaging systems that are critical to the deployment of Photometry and Remote Sensing (PaRS) capabilities in unmanned
aerial vehicles (UAVs). In general, hyperspectral data cubes include a few dozens of spectral bands that are extremely
useful for remote sensing applications that range from detection of land vegetation to monitoring of atmospheric products
derived from the processing of lower level radiance images. Because these data cubes are captured in the challenging
environment of UAVs, where resources are limited, source encoding by means of compression is a fundamental mechanism
that considerably improves the overall system performance and reliability. In this paper, we focus on the hyperspectral
images captured by a state-of-the-art commercial hyperspectral camera by showing the results of applying ultraspectral
data compression to the obtained data set. Specifically the compression scheme that we introduce integrates two stages;
(1) preprocessing and (2) compression itself. The outcomes of this procedure are linear prediction coefficients and an error
signal that, when encoded, results in a compressed version of the original image. Second, preprocessing and compression
algorithms are optimized and have their time complexity analyzed to guarantee their successful deployment using low
power ARM based embedded processors in the context of UAVs. Lastly, we compare the proposed architecture against
other well known schemes and show how the compression scheme presented in this paper outperforms all of them by
providing substantial improvement and delivering both lower compression rates and lower distortion.
One common approach to the compression of ultraspectral data cubes is by means of schemes where linear prediction plays an important role in facilitating the removal of redundant information. In general, compression algorithms can be seen as a sequence of stages where the output of one stage is the input of the following one. A stage that implements linear prediction relies heavily on a preprocessing stage that acts as a reversible procedure that rearranges the data cube and maximizes its spectral band correlation. In this paper we focus on AIRS (Atmospheric Infrared Sounder) images, a type of ultraspectral data cube, that involve more than two thousand bands and are excellent candidates to compression. Specifically we take into consideration several elements that are part of the preprocessing stage of an ultraspectral image. First, we explore the effect of SFCs (Space Filling Curves) as a way to provide a method to map an m-dimensional space into a highly correlated unidimensional space. In order to improve the overall mapping performance we propose a new scanning procedure that provides a more efficient alternative to the use of traditional state of the art curves. Second, we analyze, compare and introduce modifications to different band ordering and correlation estimation methods presented in the context of ultraspectral image preprocessing. Finally, we apply the techniques presented in this paper to a real AIRS compression architecture to obtain rate-distortion curves as a function of preprocessing parameters and determine the best scenario for a given linear prediction stage.