FPGA implementation of a maximum simplex volume algorithm for endmember extraction from remotely sensed hyperspectral images

Spectral unmixing is a very important technique for remotely sensed hyperspectral unmixing. Since more hyperspectral applications now require real or near real-time processing capabilities, fast spectral unmixing using field-programmable gate arrays (FPGAs) has received considerable interest in recent years. FPGAs can provide onboard, high computing performance at low power consumption. Another important characteristic of FPGA-based systems is reconfigurability, which makes them more flexible to process different kind of scenes. Pure signature (endmember) extraction is a fundamental step in spectral unmixing, which has been tackled using the maximum volume principle by several algorithms, most notably N-FINDR and simplex growing algorithm (SGA). These algorithms find out the simplex with maximum volume as a mechanism to extract endmembers. However, a previous dimensionality reduction step is generally required, which introduces information loss and additional computational burden. To address these issues, in this work we introduce a new volume calculation formula and further develop a new real-time implementation of a maximum simplex volume algorithm (called RT-MSVA). The proposed RT-MSVA does not need dimensionality reduction, so all spectral bands can be used without losing any information to ensure robust endmember extraction accuracy. Experiments with synthetic and real hyperspectral images have been conducted to evaluate the accuracy and computational performance of our proposed method. Our experimental results indicate that proposed FPGA-based implementation significantly outperforms the corresponding software version and achieves real-time processing performance in the considered problem. It also exhibits better endmember extraction accuracy and comparable performance to other available techniques, such as a real-time implementation of a simplex growing algorithm (RT-FSGA).