Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Abstract

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing

domain adaptation models assume that source and target do- mains are homogeneous, i.e., they have the same feature space.

Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and

better spatial coverage but has inherent weakness in differenti- ating atmospheric objects on different vertical levels. To address

this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement

by incorporating weak label information into the model (DAMA- WL). Our experiments on a collocated CALIOP and VIIRS

dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.