Robust transfer joint matching distributions in semi-supervised domain adaptation for hyperspectral images classification

Saboori, Arash; Ghassemian, Hassan

doi:10.1080/01431161.2020.1797221

Cited by 4 publications

(2 citation statements)

References 29 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%

“…An SVM-based sequential classifier training (SCT-SVM) approach was proposed for multitemporal RS image classification [74]. By casting the DA as a multitask or multiple-kernel learning problem, many multiple-kernel learning based DA methods were proposed [75]- [78]. Xu et al proposed a DA method through transferring the parameters of ELM [79].…”

Section: Classifier-based Adaptationmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Shallow Damentioning

confidence: 99%

Section: Classifier-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

View full text Add to dashboard Cite

show abstract

Reuse Out-of-Year Data to Enhance Land Cover Mapping via Feature Disentanglement and Contrastive Learning

Dantas,

Gaetano,

Paris

et al. 2025

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

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Design of Microelectronics Microsystem Neural Learning Controller Based on Subspace State Recognition

Yao

Yin

2021

Journal of Nanoelectronics and Optoelectronics

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Aiming at the large delay and large inertia in cone storage and irrigation, this paper adopts machine learning and predictive control, proposes a closed-loop N4SID (numerical subspace state system identification) algorithm, which is adjusted by IMC based on Smith predictor PI controller and parameter tuning are given, and the steps designed in cascade control are given. The experimental results verify the effectiveness of the proposed method.Compared with closed-loop SVA and open-loop methods, the method proposed in this paper has better verification results and pole assignment. According to this model, the PI controller is adjusted using IMC based on Smith Smith predictor. Compared with the traditional ZN adjustment method, this method shows good results in a conical system. In addition, the PI parameters can be adjusted to obtain robust control using the H∞-based mixed sensitivity algorithm. The contribution of this paper is the use of closed subspace system identification to identify the MIMO state space. The developed model validates the IMC tuned PI controller based on the Smith predictor in the factory.

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Robust transfer joint matching distributions in semi-supervised domain adaptation for hyperspectral images classification

Cited by 4 publications

References 29 publications

Domain Adaptation in Remote Sensing Image Classification: A Survey

Domain Adaptation in Remote Sensing Image Classification: A Survey

Reuse Out-of-Year Data to Enhance Land Cover Mapping via Feature Disentanglement and Contrastive Learning

Design of Microelectronics Microsystem Neural Learning Controller Based on Subspace State Recognition

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