Date of Award
2023
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Committee Chair
Tathagata Mukherjee
Committee Member
Aaron Kaulfus
Committee Member
Chaity Banerjee Mukherjee
Subject(s)
Big data, Machine learning, Remote-sensing images--Classification
Abstract
Advanced Deep Learning techniques have been widely used in Spatial Data Science specifically with Geographic Information Systems (GIS). Petabyte scale data production is occurring due to advances in remote sensing capabilities within the commercial and government sectors. Discovering relevant data for science research and applications can become difficult with large volumes of spatially and temporally diverse data availability. The NASA Commercial Smallsat Data Acquisition (CSDA) Program acquires and archives data from commercial satellite remote sensing vendors. While the underlying observation characteristics may be similar, such as being visible imagery, varying orbital and acquisition strategies result in a disparate data archive. This paper applies Simsiam (Simple Siamese Representation Learning) model along with the Transformer as self-supervised machine learning techniques to address the following challenges in discovering satellite imagery available through the CSDA Program: 1) the archive has a large amount of data and labeling each of scene manually for the features they contain would be tedious and time consuming, 2) Each of the vendor satellites have different instruments, orbital characteristics, and therefore different spatial, temporal, and radiometric resolutions, 3) For a given event a particular vendor may have data while others may not, and, 4) end users desire a single API or interface for the data extraction from different satellite vendors.
Recommended Citation
Paramesh, Meghana Nagarahalli, "Enabling multi-modal enhanced search using self-supervised learning on multispectral images" (2023). Theses. 616.
https://louis.uah.edu/uah-theses/616