International audienceA maximum-likelihood method for estimating hyperspectral sensors random noise components, both dependent and independent from the signal, is proposed. A hyperspectral image is locally jointly processed in the spatial and spectral dimensions within a multicomponent scanning window (MSW), as small as 7 x 7 x 7 spatial-spectral pixels. Each MSW is regarded as an additive mixture of spectrally correlated fractal Brownian motion (fBm)-samples and random noise. The main advantage of the proposed method is its ability to accurately estimate band noise variances locally by using spatial and spectral texture correlations from a single textural MSW. For each spectral band, both additive and signal-dependent band noise components are estimated by linear fit of local noise variances obtained from many MSWs distributed over the whole band intensity range. CRLB-based analysis of the estimator performance shows that a good compromise is to jointly process seven adjacent spectral bands. The proposed method performance is assessed first on synthetic fBm-data and on real images with synthesized noise. Finally, four different AVIRIS datasets from 1997 flying season are considered. Good coincidence between additive and signal-dependent AVIRIS random noise components estimates obtained by our method and the estimates retrieved from AVIRIS calibration data is demonstrated. These experiments suggest that it is worth taking into account noise signal-dependency hypothesis for processing AVIRIS data
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.