Adversarial Discriminative Active Deep Learning for Domain Adaptation in Hyperspectral Images Classification

Saboori, Arash; Ghassemian, Hassan

doi:10.1080/01431161.2021.1880663

Cited by 6 publications

(3 citation statements)

References 30 publications

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“…multiple-kernel learning [75]- [78], ELM-based [79]- [83], MDAF and MBCF [84], open set DA [85], EasyTL [86], BHC [87], DASVM [88], MRC [89], AL-based [90]- [97] Deep DA Discrepancy-based DAN [98], JAN [99], MRAN [100], DSAN [101], DeepCORAL [102], DNN with class centroid alignment [103], TCANet [104], class-wise distribution alignment based deep DA [105], DDA-Net [106], TDDA [107], TSTnet [108], GNN [109], AMF-FSL [110], MSCN [111], AMRAN [112], DJDANs [113] Adversarial-based GAN [114], [115], adversarial CNN [116], MADA [117], DAAN [118], MCD [119], DWL [120], GAN with VAE-based generator [121], [122], content-wise alignment [123], class reconstruction driven adversarial [124], class-wise adversarial [125], ADADL [126], DABAN [127], UDAD [123], deep metric learning [128], DCFSL …”

Section: Shallow Damentioning

confidence: 99%

“…Liu et al proposed a class-wise adversarial adaptation network for HSI classification, which performed class-wise adversarial learning [125]. Saboori et al proposed an adversarial discriminative active deep learning (ADADL) method for HSI classification [126]. Similar to MCD, it incorporates two different land-cover classifiers as a discriminator to consider class boundaries when aligning feature distributions, and combines the entropy measure along with the cross-entropy loss during training to use the information in unlabelled target data [126].…”

Section: B Adversarial-based Adaptationmentioning

confidence: 99%

“…Saboori et al proposed an adversarial discriminative active deep learning (ADADL) method for HSI classification [126]. Similar to MCD, it incorporates two different land-cover classifiers as a discriminator to consider class boundaries when aligning feature distributions, and combines the entropy measure along with the cross-entropy loss during training to use the information in unlabelled target data [126]. Wang [162], where the CL module is designed to select high-confidence pseudo-labeled target samples.…”

Section: B Adversarial-based Adaptationmentioning

confidence: 99%

See 2 more Smart Citations

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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