Domain Adaptation via a Task-Specific Classifier Framework for Remote Sensing Cross-Scene Classification

Zheng, Zhendong; Zhong, Yanfei; Su, Yu

doi:10.1109/tgrs.2022.3151689

Cited by 31 publications

(20 citation statements)

References 48 publications

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“…GANs are commonly used at feature maps generated from CNNs where a domain discriminator is trained to correctly classify the domain of each input feature. For example, domain-adversarial neural networks (DANNs) [34], Siamese GAN [35], Attention GAN [10], and DA via a task-specific classifier (DATSNET) framework [36] are presented for the classification of remote sensing images, by learning an invariant representation. Recently, a multitude of closed-set DA algorithms for remote sensing image scene classification [37], [38], [39], [40], [41], [42], [43] is designed to reduce the global or local distribution differences between domains.…”

Section: Related Workmentioning

confidence: 99%

Universal Domain Adaptation for Remote Sensing Image Scene Classification

Shi

Yuan

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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The domain adaptation (DA) approaches available to date are usually not well suited for practical DA scenarios of remote sensing image classification since these methods (such as unsupervised DA) rely on rich prior knowledge about the relationship between label sets of source and target domains, and source data are often not accessible due to privacy or confidentiality issues. To this end, we propose a practical universal DA (UniDA) setting for remote sensing image scene classification that requires no prior knowledge on the label sets. Furthermore, a novel UniDA method without source data is proposed for cases when the source data are unavailable. The architecture of the model is divided into two parts: the source data generation stage and the model adaptation stage. The first stage estimates the conditional distribution of source data from the pretrained model using the knowledge of class separability in the source domain and then synthesizes the source data. With this synthetic source data in hand, it becomes a UniDA task to classify a target sample correctly if it belongs to any category in the source label set or mark it as "unknown" otherwise. In the second stage, a novel transferable weight that distinguishes the shared and private label sets in each domain promotes the adaptation in the automatically discovered shared label set and recognizes the "unknown" samples successfully. Empirical results show that the proposed model is effective and practical for remote sensing image scene classification, regardless of whether the source data are available or not. The code is available at

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Section: Related Workmentioning

confidence: 99%

Universal Domain Adaptation for Remote Sensing Image Scene Classification

Shi

Yuan

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…Adayel et al developed a deep open-set DA method for cross-scene classification using adversarial learning and pareto ranking [164]. To exploit the classification information in target domain, Zheng et al proposed a DA via a taskspecific classifier (DATSNET) method for RS scene classification, where an adversarial learning strategy is used to adjust task-specific classification decision boundaries [165].…”

Section: B Adversarial-based Adaptationmentioning

confidence: 99%

Domain Adaptation in Remote Sensing Image Classification: A Survey

Peng

Huang

Sun

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Traditional remote sensing (RS) image classification methods heavily rely on labeled samples for model training. When labeled samples are unavailable or labeled samples have different distributions from that of the samples to be classified, the classification model may fail. The cross-domain or crossscene remote sensing image classification is developed for this case where an existing image for training and an unknown image from different scenes or domains for classification. The distribution inconsistency problem may be caused by the differences in acquisition environment conditions, acquisition scene, acquisition time or/and changing sensors. To cope with the cross-domain remote sensing image classification problem, many domain adaptation (DA) techniques have been developed. In this article, we review DA methods in the fields of RS, especially hyperspectral image classification, and provide a survey of DA methods into traditional shallow DA methods (e.g., instance-based, featurebased, and classifier-based adaptations) and recently developed deep DA methods (e.g., discrepancy-based and adversarial-based adaptations).

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“…GANs are commonly used at feature maps generated from CNNs where a domain discriminator is trained to correctly classify the domain of each input feature. For example, domain-adversarial neural networks (DANN) [34], Siamese GAN [35], Attention GAN [10], and domain adaptation via a task-specific classifier (DATSNET) framework [36] are presented for the classification of remote sensing images, by learning an invariant representation. Recently, a multitude of closed-set DA algorithms for remote sensing image scene classification [37]- [43] is designed to reduce the global or local distribution differences between domains.…”

Section: Related Workmentioning

confidence: 99%

Universal Domain Adaptation without Source Data for Remote Sensing Image Scene Classification

Shi

Zhu

2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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Existing domain adaptation (DA) approaches are usually not well suited for practical DA scenarios of remote sensing image classification, since these methods (such as unsupervised DA) rely on rich prior knowledge about the relationship between label sets of source and target domains, and source data are usually not accessible in many cases due to the privacy or confidentiality issues. To this end, we propose a novel source data generation-based universal domain adaptation (SDG-UniDA) model, which includes two parts, i.e., the stage of source data generation and the stage of model adaptation. The first stage is to estimate the conditional distribution of source data from the pre-trained model using the knowledge of class-separability in the source domain and then to synthesize the source data. With this synthetic source data in hand, it becomes a universal DA task that requires no prior knowledge on the label sets. A novel transferable weight is proposed to distinguish the shared and private label sets to each domain, thereby promoting the adaptation in the automatically discovered shared label set and recognizing the "unknown" samples successfully. Empirical results show that SDG-UniDA is effective and practical in this challenging setting for remote sensing image scene classification.

show abstract

Domain Adaptation via a Task-Specific Classifier Framework for Remote Sensing Cross-Scene Classification

Cited by 31 publications

References 48 publications

Universal Domain Adaptation for Remote Sensing Image Scene Classification

Universal Domain Adaptation for Remote Sensing Image Scene Classification

Domain Adaptation in Remote Sensing Image Classification: A Survey

Universal Domain Adaptation without Source Data for Remote Sensing Image Scene Classification

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