Nadir vs. Off-nadir: Initial Look at LAPAN-A3 Off-nadir Acquisition Mode on its Spectral Quality

Bayanuddin, Athar Abdurrahman; Zylshal, Zylshal; Utama, Satriya

doi:10.1109/icares.2019.8914345

Cited by 2 publications

(1 citation statement)

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“…Inevitably, difficulties and challenges in cross-scene HSI classification tasks followed. Restricted by factors such as sensor differences, imaging time, location, and atmospheric environment, the acquired HSI have heterogeneity [14]- [17]. Therefore, solving the distribution differences of the source and target domains is the key to cross-scene HSI classification, which is the domain adaptation problem.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Transformer-Based Few-Shot Learning for Cross-Domain Hyperspectral Image Classification

Peng

Liu

et al. 2023

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

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In cross-domain hyperspectral image (HSI) classification, the labeled samples of the target domain are very limited, and it is a worthy attention to obtain sufficient class information from the source domain to categorize the target domain classes (both the same and new unseen classes). This article investigates this problem by employing few-shot learning (FSL) in a meta-learning paradigm. However, most existing crossdomain FSL methods extract statistical features based on convolutional neural networks (CNNs), which typically only consider the local spatial information among features, while ignoring the global information. To make up for these shortcomings, this paper proposes novel convolutional transformer-based few-shot learning (CTFSL). Specifically, FSL is first performed in the classes of source and target domains simultaneously to build the consistent scenario. Then, a domain aligner is set up to map the source and target domains to the same dimensions. In addition, a convolutional transformer (CT) network is utilized to extract local-global features. Finally, a domain discriminator is executed subsequently that can not only reduce domain shift, but also distinguish from which domain a feature originates. Experiments on three widely used hyperspectral image datasets indicate that the proposed CTFSL method is superior to the state-of-the-art cross-domain FSL methods and several typical HSI classification methods in terms of classification accuracy.

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Section: Introductionmentioning

confidence: 99%