GPU Acclerated Automated Feature Extraction from Satellite Images

TL;DR

This paper presents a GPU-accelerated algorithm for automated feature extraction from satellite images, achieving a 20x speedup over traditional methods by leveraging parallel processing and efficient image processing techniques.

Contribution

The paper introduces a novel GPU-based implementation of feature extraction from satellite images, demonstrating significant performance improvements and practical deployment considerations.

Findings

20x speedup compared to conventional methods

Effective use of Laplacian of Gaussian masks for feature detection

Enhanced processing efficiency with GPU parallelization

Abstract

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.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.