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Hyperspectral Compressive Sensing with a System-On-Chip FPGA


3D Reconstruction in Canonical

Class Agnostic Image Common Ob
Abstract


Advances in hyperspectral sensors have led to a significantly increased capability for high-quality data. This trend calls for the development of new techniques to enhance the way that such unprecedented volumes of data are stored, processed, and transmitted to the ground station. An important approach to deal with massive volumes of information is an emerging technique, called compressive sensing, which acquire directly the compressed signal instead of acquiring the full data set. Thus reducing the amount of data that needs to be measured, transmitted and stored in first place. In this paper, a hardware/software implementation in a systemon- chip Field-Programmable Gate Array (FPGA) for compressive sensing is proposed. The proposed hardware/software architecture runs the compressive sensing algorithm with a unitary compression rate over an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor image with 512 lines, 614 samples, 224 bands in 0.35 seconds. The proposed system runs 49 and 216 faster than an embedded 256-cores GPU of a Jetson TX2 board and the ARM of the system-on-chip FPGA, respectively. In terms of energy, the proposed architecture requires around 100 less energy.

KeyWords
Hyperspectral Imagery, Compressive Sensing, Field-Programmable Gate Arrays (FPGA), On-board Processing, Real-Time.



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