GPU Accelerated Automated Feature Extraction From Satellite Images

The availability of large volumes of remote sensing data insists on higher degree of automation in feature extraction, making it a need of the hour. Utilizing the aforementioned features in remote sensing is impracticable in the absence of automation. While efforts are underway to reduce human intervention in data processing, this attempt alone may not suffice. The huge quantum of data that needs to be processed entails accelerated processing to be enabled. GPUs, which were originally designed to provide efficient visualization, are being massively employed for computation intensive parallel processing environments. This paper discusses the aforesaid algorithm for automated feature extraction, necessity of deployment of GPUs for the same; system-level challenges and quantifies the benefits of integrating GPUs in such environment. The results demonstrate that substantial enhancement in performance margin can be achieved with the best utilization of GPU resources and an efficient parallelization strategy. Performance results in comparison with the conventional computing scenario have provided a speedup of 20x, on realization of this parallelizing strategy.