Adapting Remote Sensing to New Domain With ELM Parameter Transfer

Xu, Suhui; Mu, Xiaodong; Chai, Dong; Wang, Shuyang

doi:10.1109/lgrs.2017.2726760

Cited by 13 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…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]. Considering the simplicity of ELM, many ELMbased classifier adaptation methods were proposed, such as cross domain ELM (CDELM) [80], ELM-based heterogeneous DA [81], interpretable rule-based fuzzy ELM (IRF-ELM) [82], ensemble transfer learning based on ELM (TL-ELM) [ approach by exploiting intra-domain structures to learn both nonparametric transfer features and classifiers [86].…”

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

View full text Add to dashboard Cite

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).

show abstract

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

“…As a result, the idea of exploiting the availability of labeled data in one or more domain to predict unlabeled data in another domain has emerged, and is known as "domain adaptation". Unlike several machine learning algorithms, which assume that the training and testing samples are drawn from the same distribution [32], training and testing data in domain adaptation have different distributions, that is, the training images are always with labels and are extracted from what is called the source domain, while the test images are without (or with few) labels and are called the target domain [1]. The main goal of domain adaptation is to mitigate the distribution discrepancy between the source and target domains [33].…”

Section: Introductionmentioning

confidence: 99%

Learning a Multi-Branch Neural Network from Multiple Sources for Knowledge Adaptation in Remote Sensing Imagery

et al. 2018

View full text Add to dashboard Cite

In this paper we propose a multi-branch neural network, called MB-Net, for solving the problem of knowledge adaptation from multiple remote sensing scene datasets acquired with different sensors over diverse locations and manually labeled with different experts. Our aim is to learn invariant feature representations from multiple source domains with labeled images and one target domain with unlabeled images. To this end, we define for MB-Net an objective function that mitigates the multiple domain shifts at both feature representation and decision levels, while retaining the ability to discriminate between different land-cover classes. The complete architecture is trainable end-to-end via the backpropagation algorithm. In the experiments, we demonstrate the effectiveness of the proposed method on a new multiple domain dataset created from four heterogonous scene datasets well known to the remote sensing community, namely, the University of California (UC-Merced) dataset, the Aerial Image dataset (AID), the PatternNet dataset, and the Northwestern Polytechnical University (NWPU) dataset. In particular, this method boosts the average accuracy over all transfer scenarios up to 89.05% compared to standard architecture based only on cross-entropy loss, which yields an average accuracy of 78.53%.

show abstract