GPU Acclerated Automated Feature Extraction from Satellite Images
K. Phani Tejaswi, D. Shanmukha Rao, Thara Nair, A. V. V. Prasad

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
