high performance computing on graphics processing units: hgpu.org

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. Fusing data from multiple sources, such as panchromatic, hyper spectral and LiDAR sensors, enhances the probability of identifying and extracting features such as buildings, vegetation or bodies of water by using a combination of spectral and elevation characteristics. 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. Image processing in general and hence automated feature extraction, is highly computation intensive, where performance improvements have a direct impact on societal needs. In this context, an algorithm has been formulated for automated feature extraction from a panchromatic or multispectral image based on image processing techniques. Two Laplacian of Guassian (LoG) masks were applied on the image individually followed by detection of zero crossing points and extracting the pixels based on their standard deviation with the surrounding pixels. The two extracted images with different LoG masks were combined together which resulted in an image with the extracted features and edges. Finally the user is at liberty to apply the image smoothing step depending on the noise content in the extracted image. The image is passed through a hybrid median filter to remove the salt and pepper noise from the image. 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.