  Semi-supervised domain adaptation methods leverage information from a source
labelled domain with the goal of generalizing over a scarcely labelled target
domain. While this setting already poses challenges due to potential
distribution shifts between domains, an even more complex scenario arises when
source and target data differs in modality representation (e.g. they are
acquired by sensors with different characteristics). For instance, in remote
sensing, images may be collected via various acquisition modes (e.g. optical or
radar), different spectral characteristics (e.g. RGB or multi-spectral) and
spatial resolutions. Such a setting is denoted as Semi-Supervised Heterogeneous
Domain Adaptation (SSHDA) and it exhibits an even more severe distribution
shift due to modality heterogeneity across domains.To cope with the challenging
SSHDA setting, here we introduce SHeDD (Semi-supervised Heterogeneous Domain
Adaptation via Disentanglement) an end-to-end neural framework tailored to
learning a target domain classifier by leveraging both labelled and unlabelled
data from heterogeneous data sources. SHeDD is designed to effectively
disentangle domain-invariant representations, relevant for the downstream task,
from domain-specific information, that can hinder the cross-modality transfer.
Additionally, SHeDD adopts an augmentation-based consistency regularization
mechanism that takes advantages of reliable pseudo-labels on the unlabelled
target samples to further boost its generalization ability on the target
domain. Empirical evaluations on two remote sensing benchmarks, encompassing
heterogeneous data in terms of acquisition modes and spectral/spatial
resolutions, demonstrate the quality of SHeDD compared to both baseline and
state-of-the-art competing approaches. Our code is publicly available here:
https://github.com/tanodino/SSHDA/
