# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 2 Related Work
### Domain Adaptation in Remote Sensing Applications
A common technique employed by conventional UDA schemes is _alignment_, i.e., transforming either raw inputs or features such that the resultant probability distributions (marginal or/and conditional) in the source and target domains are as close as possible [8; 52]. The closeness of distributions can be quantified with a divergence measure, e.g., Kullback-Leibler divergence [68]. At least two classes of deep, conventional UDA methods are discernible [44; 46]:  
1. Discrepancy-based methods perform _statistical divergence alignment_[45], i.e., match marginal or/and conditional distributions between domains by integrating into a DNN adaptation layer designed to minimise domain discrepancy in a latent feature space. See [46] for a survey of applications of discrepancy-based UDA to EO image classification. 2. Adversarial-learning methods learn transferable and domain-invariant features through adversarial learning. A well-known method is using a domain-adversarial neural network (DANN) [58], which comprises a feature extractor network connected to a label predictor and a domain classifier. Training the network parameters to (i) minimise the loss of the label predictor but (ii) maximise the loss of the domain classifier promotes the emergence of domain-invariant features. See [46] for a survey of applications of adversarial-learning UDA to EO image classification. Sample applications of UDA to cloud detection are compared in Table 2. None of the methods covered so far are applicable when (i) target data are unavailable during training, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Source Dom.** & **Target Dom.** & **Characteristics** \\ \hline \multirow{3}{*}{[56]\({}^{*}\) WorldView-2 Sentinel-2} & \multirow{3}{*}{Proba-V} & A DeepLab-like [57] CNN is trained on the source data and adapted to the target domain through a Domain-Adversarial Neural Network [58]. \\  & & & A five-layer fully connected neural network is trained on an upscaled version of the source data, and adapted to the target domain through generative domain mapping [45], where a cycle-consistent generative adversarial network [60] maps target data to the upscaled source domain. \\ \hline \multirow{3}{*}{[61]\({}^{*}\) Gooding Earth} & \multirow{3}{*}{\begin{tabular}{c} WorldView-2 Google Earth \\ Google Earth \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} Google Earth \\ WorldView-2 \\ \end{tabular} } & Image level: Pseudo-target-domain data generator fuses source-domain foreground information with target-domain background information. Feature level: (i) Global feature alignment based on domain discrimination reduces global domain shift. (ii) Decision optimisation based on self-ensembling consistency [62] reduces local domain shift. \\ \hline \multirow{3}{*}{[63]\({}^{*}\) Dioni} & \multirow{3}{*}{\
\begin{tabular}{c} HyRANK, Pavia \\ \end{tabular} } & A 3D-CNN [64] is trained on the source data and adapted to the target domain through contrastive prototype generation and adaptation (CGPA) [65]. \\ \hline \multirow{3}{*}{[66]\({}^{*}\) Gooding Earth (rural)} & \multirow{3}{*}{
\begin{tabular}{c} HyRANK, Pavia \\ \end{tabular} } & Attention-guided prompt tuning: Uses the encoder of a vision foundation model as the backbone network (with embedded target prompts) and the decoder of UperNet [67] as the segmentation head. Loss function is the cosine similarity between the source prototypes and target features filtered by the pseudo-labels (generated by the source-trained model) with high prediction confidence. Optimising the loss function trains source and target attention matrices to select layers that are relevant to the current task and target prompts to bring target features close to source features. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont_. TTA  
TTA is UDA without access to the source data.