FEW-Shot Cross-Sensor Domain Adaptation Between SAR and Multispectral Data

K. Ram Prabhakar, Veera Harikrishna Nukala, Jayavardhana Gubbi, Arpan Pal, Balamuralidhar P

Abstract: In this paper, we present a novel few-shot cross-sensor domain adaptation technique between SAR and multispectral data for LULC classification. Cross-sensor, such as SAR and multispectral, domain adaptation is a long standing challenge in remote sensing. Due to scarcity of large annotated dataset for every domain, it is desirable to have a method that enables cross-domain training with limited supervisory signal in that domain. We address this problem in this paper with a novel few-shot domain adaptation technique. We leverage large corpus of annotated multispectral dataset to improve performance for SAR based LULC classification. We propose a novel Feature Domain Alignment (FDA) loss function to align higher dimension features between multispectral and SAR domain. We validate our approach in publicly available DFC2020 dataset and achieve 78% overall LULC classification accuracy using only 5% annotated SAR samples.