Deep Transfer Learning for Few-Shot SAR Image Classification

By Mohammad Rostami, Soheil Kolouri, Eric Eaton, Kyungnam Kim. 2019

199 citations recorded by the source corpus.

The reemergence of Deep Neural Networks (DNNs) has lead to high-performance supervised learning algorithms for the Electro-Optical (EO) domain classification and detection problems. This success is because generating huge labeled datasets has become possible using modern crowdsourcing labeling platforms such as Amazon’s Mechanical Turk that recruit ordinary people to label data. Unlike the EO domain, labeling the Synthetic Aperture Radar (SAR) domain data can be much more challenging, and for various reasons, using crowdsourcing platforms is not feasible for labeling the SAR domain data. As a result, training deep networks using supervised learning is more challenging in the SAR domain. In the paper, we present a new framework to train a deep neural network for classifying Synthetic Aperture Radar (SAR) images by eliminating the need for a huge labeled dataset. Our idea is based on transferring knowledge from a related EO domain problem, where labeled data are easy to obtain. We transfer knowledge from the EO domain through learning a shared invariant cross-domain embedding space that is also discriminative for classification. To this end, we train two deep encoders that are coupled through their last year to map data points from the EO and the SAR domains to the shared embedding space such that the distance between the distributions of the two domains is minimized in the latent embedding space. We use the Sliced Wasserstein Distance (SWD) to measure and minimize the distance between these two distributions and use a limited number of SAR label data points to match the distributions class-conditionally. As a result of this training procedure, a classifier trained from the embedding space to the label space using mostly the EO data would generalize well on the SAR domain. We provide a theoretical analysis to demonstrate why our approach is effective and validate our algorithm on the problem of ship classification in the SAR domain by comparing against several other competing learning approaches.

Subfields: Aerospace Engineering, Artificial Intelligence, Oceanography

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

In Artificial Intelligence

In Oceanography

Citations in the corpus

Unsupervised Model Adaptation for Continual Semantic Segmentation (2020, methodology): “Wasserstein Distance (WD) has been used successfully for domain alignment in the UDA literature (Bhushan Damodaran et al. 2018b; Rostami et al. 2019; Xu et al. 2020; Li et al. 2020; Rostami and Galstyan 2020).”
Learning Transferable Knowledge Through Embedding Spaces (2019, unknown): “Results of this chapter have been presented in [142; 143; 144; 145].”
Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation (2021, background): “[49] designed a network with two deep encoders that were coupled to transfer the electrooptical (EO) domain knowledge to the SAR domain.”
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment (2019, background): “conditioned probability distributions in the embedding space, then a classifier trained solely using labeled data from the source domain will generalize well on data points that are drawn from the target domain distribution [28], [30].”