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2017 GTC San Jose

S7472 - Comparative Study of CNN Models for Detection of Clouds in Overhead Imagery

Session Speakers
Session Description

Learn how to improve pixel-wise image quality and geolocation accuracy by leveraging high-end hybrid computing resources. This particular test case involves the use of deep learning in the detection and masking of cloud objects, and imagery content that reduces image quality and usability, from overhead imagery. Timely results are attained through expediting selection and deployment of a deep learning model for overhead imagery for the cloud detection problem. An optimum deep learning model is selected through evaluation of a set of convolutional neural networks for their ability to detect cloud objects. Evaluation of each network is performed using a number of open-source neural network packages to give comparative performance results. In addition, two complementary image segmentation techniques are implemented in parallel, one operating on CPUs and the other on GPUs, to rapidly obtain candidate regions for cloud objects at a fine resolution.


Additional Session Information
Intermediate
Talk
Deep Learning and AI HPC and Supercomputing
Defense
25 minutes
Session Schedule