FPGA Implementation of a Hardware Optimized Automatic Target Detection and Classification Algorithm for Hyperspectral Image Analysis

In hyperspectral image (HSI) analysis, one of the most important tasks is target detection, requiring the execution of algorithms with high computational complexity. Recently, research efforts have focused on on-board real-time target detection to provide timely responses for swift decisions. Therefore, it is necessary to use a technology that provides the performance needed for real-time target detection, and at the same time meets the satellite payload requirements. Field-programmable gate arrays (FPGAs) have very interesting properties in terms of performance, size, and power consumption, which have become the standard option for on-board processing. In this letter, we present a hardware optimized implementation for FPGAs of the automatic target detection and classification algorithm (ATDCA) using the Gram–Schmidt (GS) method for orthogonalization purposes. The ATDCA-GS algorithm is directly coded using VHDL and verified on a Virtex-7 XC7VX690T FPGA using real hyperspectral data [collected by Hyperspectral Digital Imagery Collection Experiment (HYDICE) sensor and by NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS)] and a synthetic image. Experimental results demonstrate that our hardware version of the ATDCA-GS algorithm outperforms previous implementations (multicore processors, GPUs, and accelerators) in both computation time (obtaining real-time performance) and power consumption, demonstrating the suitability of FPGAs for this purpose.