301 research outputs found
Exploiting Structural Complexity for Robust and Rapid Hyperspectral Imaging
This paper presents several strategies for spectral de-noising of
hyperspectral images and hypercube reconstruction from a limited number of
tomographic measurements. In particular we show that the non-noisy spectral
data, when stacked across the spectral dimension, exhibits low-rank. On the
other hand, under the same representation, the spectral noise exhibits a banded
structure. Motivated by this we show that the de-noised spectral data and the
unknown spectral noise and the respective bands can be simultaneously estimated
through the use of a low-rank and simultaneous sparse minimization operation
without prior knowledge of the noisy bands. This result is novel for for
hyperspectral imaging applications. In addition, we show that imaging for the
Computed Tomography Imaging Systems (CTIS) can be improved under limited angle
tomography by using low-rank penalization. For both of these cases we exploit
the recent results in the theory of low-rank matrix completion using nuclear
norm minimization
Spectral-Spatial Method for Hyperspectral Image classification in Noisy Environment
International audienc
Wavelet-Based Multicomponent Denoising Profile for the Classification of Hyperspectral Images
The high resolution of the hyperspectral remote sensing images available allows the detailed analysis of even small spatial structures. As a consequence, the study of techniques to efficiently extract spatial information is a very active realm. In this paper, we propose a novel denoising wavelet-based profile for the extraction of spatial information that does not require parameters fixed by the user. Over each band obtained by a wavelet-based feature extraction technique, a denoising profile (DP) is built through the recursive application of discrete wavelet transforms followed by a thresholding process. Each component of the DP consists of features reconstructed by recursively applying inverse wavelet transforms to the thresholded coefficients. Several thresholding methods are explored. In order to show the effectiveness of the extended DP (EDP), we propose a classification scheme based on the computation of the EDP and supervised classification by extreme learning machine. The obtained results are compared to other state-of-the-art methods based on profiles in the literature. An additional study of behavior in the presence of added noise is also performed showing the high reliability of the EDP proposedThis work was supported in part by the Consellería de Educación, Universidade e Formación Profesional under Grants GRC2014/008 and ED431C 2018/2019 and the Ministerio de Economía y Empresa, Gobierno de España under Grant TIN2016-76373-P. Both are cofunded by the European Regional Development FundS
Generalized Inpainting Method for Hyperspectral Image Acquisition
A recently designed hyperspectral imaging device enables multiplexed
acquisition of an entire data volume in a single snapshot thanks to
monolithically-integrated spectral filters. Such an agile imaging technique
comes at the cost of a reduced spatial resolution and the need for a
demosaicing procedure on its interleaved data. In this work, we address both
issues and propose an approach inspired by recent developments in compressed
sensing and analysis sparse models. We formulate our superresolution and
demosaicing task as a 3-D generalized inpainting problem. Interestingly, the
target spatial resolution can be adjusted for mitigating the compression level
of our sensing. The reconstruction procedure uses a fast greedy method called
Pseudo-inverse IHT. We also show on simulations that a random arrangement of
the spectral filters on the sensor is preferable to regular mosaic layout as it
improves the quality of the reconstruction. The efficiency of our technique is
demonstrated through numerical experiments on both synthetic and real data as
acquired by the snapshot imager.Comment: Keywords: Hyperspectral, inpainting, iterative hard thresholding,
sparse models, CMOS, Fabry-P\'ero
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