A Two-Stage Adaptation Network (TSAN) for Remote Sensing Scene Classification in Single-Source-Mixed-Multiple-Target Domain Adaptation (S²M²T DA) Scenarios

Semi-supervised bidirectional alignment for Remote Sensing cross-domain scene classification

“…In recent years, an increasing number of works have focused on the domain adaptation (DA) of RS image scene classification ( Song et al, 2019 , Lu et al, 2020 , Zheng et al, 2021 , Zhang et al, 2020 , Zhu et al, 2021 , Zheng et al, 2022a , Lasloum et al, 2021 ). In Song et al (2019) , a new subspace alignment layer added into CNN models was proposed for the DA of RS image scene classification to align the source domain and the target domain in the feature subspace; as a result, it can optimize CNN models to adapt to the classification of the target domain.…”

“…In AMRAN, both marginal and conditional distributions were taken into consideration, and the multiscale attention mechanism was used to extract robust features and complete information. In Zheng et al (2022a) , the single-source multiple-target domain adaptation task was explored for RS applications and a new algorithm named two-stage adaptation network (TSAN) was presented, which: (1) utilizes the adversarial learning approach to confuse the classifier between the source domain and the whole mixed multi-target domain, and (2) adopts self-supervised learning to divide the mixed-multiple-target domain with its pseudo domain labels in order to learn intrinsic features of multiple target domains. In Lasloum et al (2021) , the MME algorithm ( Saito et al, 2019 ) mentioned in Section 2.1 was applied to multi-source SSDA for the purpose of RS image scene classification.…”

Convolutional Transformer-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification

“…In cross-scene HSI classification, domain adaption aims to transfer data knowledge from the source domain to the target domain by mapping the data features of two domains into the same feature space [34], [35]. Domain adaptation can solve the distribution discrepancy between the source and target domains by learning domain-invariant features.…”

Domain Adaptation in Remote Sensing Image Classification: A Survey

“…TriADA [132], SCDAL [133], TSAN [134], DANN [135], [136], StandardGAN [137] Others two-branch CNN [138], FANN [139], DATL [140], MSSN [141], active transfer learning network [140], AALDA [142], attention-based residual network [143], 3CN [144], bayesianinspired CNN [145] and the high dimensionality of the new representations. To solve these problems, geodesic flow kernel (GFK) method was proposed [13].…”

“…Liu et al proposed an unsupervised adversarial DA network for remotely sensed scene classification, where a GAN model based feature extractor makes the source and target distributions closer, and a transferred classifier trained by transferred source domain features is able to acquire a better classification accuracy on the target domain [163]. Zheng et al proposed a twostage adaptation network (TSAN) for RS scene classification considering single source domain and multiple target domains [134], which utilizes the adversarial learning to align single source features with mixed-multiple-target features and selfsupervised learning to distinguish the mixed-multiple-target domain. Adayel et al developed a deep open-set DA method for cross-scene classification using adversarial learning and pareto ranking [164].…”