Abstract
A problem of lossy compression of hyperspectral images is considered. A specific aspect is that we assume a signal-dependent model of noise for data acquired by new generation sensors. Moreover, a signal-dependent component of the noise is assumed dominant compared to a signal-independent noise component. Sub-band (component-wise) lossy compression is studied first, and it is demonstrated that optimal operation point (OOP) can exist. For such OOP, the mean square error between compressed and noise-free images attains global or, at least, local minimum, i.e., a good effect of noise removal (filtering) is reached. In practice, we show how compression in the neighborhood of OOP can be carried out, when a noise-free image is not available. Two approaches for reaching this goal are studied. First, lossy compression directly applied to the original data is considered. According to another approach, lossy compression is applied to images after direct variance stabilizing transform (VST) with properly adjusted parameters. Inverse VST has to be performed only after data decompression. It is shown that the second approach has certain advantages. One of them is that the quantization step for a coder can be set the same for all sub-band images. This offers favorable prerequisites for applying three-dimensional (3-D) methods of lossy compression for sub-band images combined into groups after VST. Two approaches to 3-D compression, based on the discrete cosine transform, are proposed and studied. A first approach presumes obtaining the reference and “difference” images for each group. A second performs compression directly for sub-images in a group. We show that it is a good choice to have 16 sub-images in each group. The abovementioned approaches are tested for Hyperion hyperspectral data. It is demonstrated that the compression ratio of about 15–20 can be provided for hyperspectral image compression in the neighborhood of OOP for 3-D coders, which is sufficiently larger than for component-wise compression and lossless coding.
Highlights
Airborne and space-borne hyperspectral imaging is a useful and conventional tool in remote sensing (RS)
MSEtc is larger while MSSIMtc and peak signal-to-noise ratio (PSNR)-HVS-Mtc are smaller; these changes relate to a worse quality of decompressed images according to the considered metrics; this means that if noise intensity in the original images is higher, the quality of images compressed in operation point (OOP) is lower; this property could be predicted in advance; 3
We used quantization step (QS) 1⁄4 3.5 in all our experiments described
Summary
Airborne and space-borne hyperspectral imaging is a useful and conventional tool in remote sensing (RS). One and probably the best (the most accurate and simplest) way to estimate the parameters σ20ðnÞ and kðnÞ is to analyze calibration data for acquired hyperspectral images.[14] calibration data are not always available in practice This means that one has to directly apply a blind method for obtaining the estimates of σ^ 20ðnÞ and k^ðnÞ from acquired images. One more important observation is that using the obtained estimates, it is possible to evaluate the contributions of SD and SI noise components Such analysis has shown that noise variance induced by an SD component is usually considerably (up to 40 times) greater than the SI noise variance at the upper margin of the data dynamic range for almost all sub-band images. This has been first noticed in the paper 9 for such standard criterion (metric) as mean square error
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
