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.
AIRS (Atmospheric Infrared Sounder) images are a type of ultraspectral data cubes that are good candidates for compression
as they include several thousand bands that account for well over 40MB of information per image. In this paper
we describe and mathematically model an improved architecture to accomplish lossy compression of AIRS images by
presenting a sequence of techniques executed under the context of preprocessing and compression stages. Specifically
we describe both a preprocessing reversible stage that rearranges the AIRS data cube and a linear prediction based compression
stage that improves the compression rate when compared to other state of the art ultraspectral data compression
techniques. After defining a distortion measure as well as its effect on real applications (i.e. AIRS Level 2 products) we
present a mathematical model to approximate the rate-distortion of the architecture and compare it against the experimental
performance of the algorithm. The analysis relies on the vector quantization of the prediction error and assumes that the
individual samples follow a Laplacian distribution that is the only source of distortion. In general under an open-loop
encoding scheme, the distortion caused by the quantization of linear-prediction coefficients is masked by the distortion
introduced by the prediction error itself. The effect of the preprocessing stage on the analytical model is accounted by
different values of the Laplacian distribution such that the curve obtained by parametrically plotting rate against distortion
is a close approximation of the experimental one.
We propose a new architecture to accomplish lossy ultraspectral data compression where we particularly focus on AIRS
(Atmospheric Infrared Sounder) images. In general AIRS images are good candidates for compression as they include more
than two thousand spectral bands that account for over 40MB of data per single data cube. In our proposed compression
technique the input image is first preprocessed by means of spatial subband decomposition followed by a spectral band
ordering stage which is applied in order to increase the correlation between contiguous spectral bands. The resulting image
is segmented on a spectral band basis in such a way that spectral bands are scanned to generate a speech-like signal that
exhibits a higher spectral interband than intraband correlation and therefore can be modeled as an AR (autoregressive)
process. As final step the data is processed through a compression stage involving short and long term forward linear
prediction that produces an error signal that is encoded using a CELP (Code Excited Linear Prediction) scheme. The
forward linear prediction filter order and the resolution of the CELP codebooks are adjusted depending on the spatial
subband that originates the signal being predicted. By manipulating several parameters of both the preprocessing and
compression stages different rate-distortion curves are obtained and highly efficient compression is achieved.
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