A Micro-Mirror Array based System for Compressive Sensing of Hyperspectral Data (original) (raw)
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Compressive Sensing-based technologies have shown a great potential to improve the efficiency of acquisition, manipulation , analysis and storage processes on signals and imagery with little discernible loss in data performance. The CS framework is based on the assumption that signals are sparse in some domain and can be reconstructed from a significantly reduced amount of samples. As a result, a solution to the underdetermined linear system resulting from this paradigm makes it possible to estimate the original signal with high accuracy using linear programming techniques. This paper presents a study on the use of compressive sensing on satellite Hyperspectral Images, which provide a variety of fields and applications with data with a high information density for analysis. Hyperspectral imaging of large areas at high resolutions required for some applications can turn the image capturing, processing and storage processes into a time consuming procedure, presenting a limitation for use in resource-limited or time-sensitive settings. We present an analysis on the algorithm parametrization that may allow for a simpler capturing approach tailored specifically for a given application's needs using the well-studied l1-magic algorithm. We provide a comparative study in compressive sensing and estimate its effectiveness in terms of compression ratio vs. image reconstruction accuracy. Preliminary results show that by using as little as 25% of the original number of samples, large structures may be reconstructed with high accuracy.
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Compressive hyperspectral imaging is based on the fact that hyperspectral data is highly redundant. However, there is no symmetry between the compressibility of the spatial and spectral domains, and that should be taken into account for optimal compressive hyperspectral imaging system design. Here we present a study of the influence of the ratio between the compression in the spatial and spectral domains on the performance of a 3D separable compressive hyperspectral imaging method we recently developed. Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/01/2013 Terms of Use: http://spiedl.org/terms Proc. of SPIE Vol. 8717 87170G-2 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/01/2013 Terms of Use: http://spiedl.org/terms Proc. of SPIE Vol. 8717 87170G-9 Downloaded From: http://proceedings.spiedigitallibrary.org/ on 06/01/2013 Terms of Use: http://spiedl.org/terms
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An efficient method and system for compressive sensing of hyperspectral data is presented. Compression efficiency is achieved by randomly encoding both the spatial and the spectral domains of the hyperspectral datacube. Separable sensing architecture is used to reduce the computational complexity associated with the compressive sensing of a large volume of data, which is typical of hyperspectral imaging. The system enables optimizing the ratio between the spatial and the spectral compression sensing ratios. The method is demonstrated by simulations performed on real hyperspectral data.
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Recently we introduced a hyperspectral compressive sensing scheme that uses separable projections in the spatial and spectral domains. The separable encoding schemes facilitates the optical implementation, reduces the computational burden dramatically, and storage requirements. Owing to these benefits we have been able to encode the hyperspectral cube in all three dimensions. In this work we present a comparison between various reconstructions methods applied to the hyperspectral data captured with our separable compressive sensing systems.
A new technique for hyperspectral compressive sensing using spectral unmixing
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In hyperspectral imaging, the instruments measure the light reflected by the Earth surface in hundreds or thousands of spectral bands, generating huge amounts of data that must be effectively processed. The real-time requirements of some applications demand large bandwidths between the sensor and the ground stations. In order to simplify the hardware and software requirements of the hyperspectral acquisition systems, we develop a compressive sensing (CS) based technique for hyperspectral image reconstruction. CS is applicable when the data is compressible (or sparse) in a given basis or frame. This is usually the case with hyperspectral images as a consequence of its high correlation. The hyperspectral images which are compressible can be recovered from a number of measurements much smaller than the size of the original data. This compressed version of the data can then be sent to a ground station that will recover the original image by running a reconstruction algorithm. Specifically, in this work we elaborate on a previously introduced hyperspectral coded aperture (HYCA) algorithm. The performance of HYCA relies on the tuning of a regularization parameter, which is a time consuming task. Herein, we introduce a constrained formulation of HYCA, termed constrained HYCA (C-HYCA), which does not depend on any regularization parameter. C-HYCA optimization is solved with the C-SALSA alternating direction method of multipliers. In a series of experiments with simulated and real data we show that C-HYCA performance is similar to that of HYCA obtained with the best regularization parameter setting.