Deep Convolutional and Generative Networks for Ocean Synoptic Feature Extraction and Super Resolution from Remotely Sensed Images

• Main Author: Lambhate, Devyani
• Other Authors: Subramani, Deepak N
• Format: Thesis
• Language: English
• Published: 2022
• Subjects: remote-sensing; ocean data; Deep learning algorithms; ocean synoptic feature extraction; W-Net; Sea Surface Temperature; Research Subject Categories::TECHNOLOGY::Information technology::Computer science; North Atlantic
• Online Access: https://etd.iisc.ac.in/handle/2005/5683

Accurate extraction of Synoptic Ocean Features and Downscaling of Ocean Features is crucial for climate studies and the operational forecasting of ocean systems. With the advancement of space and sensor technologies, the amount of remote-sensing ocean data is rising sharply. There is a need for precise and reliable algorithms to extract information from such remotely sensed datasets. Deep learning algorithms have shown significant superiority over traditional physical or statistical methods for several remote-sensing applications. Two important applications are ocean synoptic feature extraction (needed to extract useful information submerged in data) and downscaling of satellite images (needed due to insufficient resolution of current imaging sensors). This thesis introduces two novel deep learning algorithms: W-Net (for Ocean Feature Extraction) and PF-GAN-SR (for Downscaling of Sea Surface Temperature Satellite Images). Ocean Synoptic Feature Extraction: For operational regional models of the North Atlantic, skilled human operators visualize and extract the Gulf Stream and Rings (Warm and Cold Eddies) through a time-consuming manual process. There is a need for an automated dynamics-inspired system to extract Gulf Stream and Rings. We have developed a deep learning system (W-Net) that extracts the Gulf Stream and Rings from concurrent satellite images of sea surface temperature (SST) and sea surface height (SSH). Our approach's novelty is that the above extraction task is posed as a multi-label semantic image segmentation problem solved by developing and applying a deep convolutional neural network with two parallel Encoder-Decoder networks (one branch for SST and the other for SSH), implemented as a WNet. W-Net is the first neural architecture and deep learning system developed for automated synoptic ocean feature segmentation. For the Gulf Stream, we obtain 82.7% raw test accuracy and a low error of 4.39% in the detected path length. For the Rings, we obtain more than 71% raw eddy detection accuracy. ...