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- Hyperspectral Image Classification Based on Domain Adaptation Broad . . .
Therefore, we propose an HSI classification method based on domain adaptation broad learning (DABL) First, according to the importance of the marginal and conditional distributions, the maximum mean discrepancy is used in mapped features to adapt these distributions between source and target domains
- Active multi-kernel domain adaptation for hyperspectral image . . .
To address this issues, in this paper we show a novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL)
- Cross-domain Hyperspectral Image Classification based on Bi-directional . . .
we propose a Bi-directional Domain Adaptation (BiDA) frame-work for cross-domain hyperspectral image (HSI) classification, which focuses on extracting both domain-invariant features and domain-specific information in the independent adaptive space, thereby enhancing the adaptability and separability to the target scene
- Hyperspectral Image Classification Based on Domain Adversarial Broad . . .
This project is the implementation of Hyperspectral Image Classification based on DABAN Architecture as mentioned in the research paper provided The system consists of two main components: 1 Domain Adversarial Adaptation Network (DAAN) 2 Conditional Adaptation Broad Network (CABN)
- Hyperspectral Image Classification Based On Domain Adaptation Broad . . .
Therefore, we propose a HSI classification method based on domain adaptation broad learning (DABL)
- Confident Learning-Based Domain Adaptation for Hyperspectral Image . . .
Abstract: Cross-domain hyperspectral image classification is one of the major challenges in remote sensing, especially for target domain data without labels Recently, deep learning approaches have demonstrated effectiveness in domain adaptation
- Cross-domain Hyperspectral Image Classification based on Bi-directional . . .
Specifically, the source branch and target branch independently learn the adaptive space of source and target domains, a Coupled Multi-head Cross-attention (CMCA) mechanism is developed in coupled branch for feature interaction and inter-domain correlation mining
- ChiWang-echo Domain-Adaptation - GitHub
Contribute to ChiWang-echo Domain-Adaptation development by creating an account on GitHub
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