A Comparative Study on the Parametrization of a Block-based Compressive Sensing Algorithm for Hyperspectral Imaging Applications

Abstract

Abstract-Compressive Sensing-based technologies have shown a great potential to improve the efficiency of acquisition, manipu-lation, 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. Abstract-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 resourcelimited 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 struc...