Local–Global Active Learning Based on a Graph Convolutional Network for Semi-Supervised Classification of Hyperspectral Imagery

Ye, Zhen; Sun, Tao; Shi, Shihao; Fowler, James E.

doi:10.1109/lgrs.2023.3244758

Cited by 10 publications

(1 citation statement)

References 14 publications

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“…Conversely, the pseudo-labeling approach leverages, such as CVA or multilayer cascade screening [36] to leverage unlabeled samples. This strategic approach empowers deep learning models, including the utilization of graph neural networks [37], to be trained on a combination of real-labeled and pseudo-labeled training samples, effectively addressing the challenge posed by the limited availability of labeled data [38]. This strategic fusion optimally harnesses the strengths of both labeled and unlabeled data, resulting in a substantial enhancement in the accuracy of change detection algorithms.…”

Section: A Semi-supervised Learningmentioning

confidence: 99%

Semi-Supervised HyperMatch-Driven Cross Temporal and Spatial Interaction Transformer for Hyperspectral Change Detection

Huang,

Zhang,

et al. 2024

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

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Hyperspectral images are valuable for precise land cover change detection in a consistent area over time. Nevertheless, supervised methods for hyperspectral change detection face limitations due to insufficient labeled samples. Additionally, current deep learning-based approaches often neglect critical feature interactions across temporal, spatial, and spectral domains. To address these challenges, we introduce a semisupervised model called the HyperMatch-based Cross Temporal and Spatial Interaction Transformer (CTSIT) for hyperspectral change detection. The key contributions of this study are as follows: 1) Introduction of the HyperMatch training schedule, which relies on weak-to-strong consistency, to enhance feature extraction using both labeled and unlabeled data. 2) Contribution of the Cross Temporal Bidirectional Attention (CTBA) module to emphasize bidirectional temporal interactions and resemblances. 3) Introduction of the Spatial and Spectral Attention (SSA) module, which includes the Dense Cross Spatial Attention (DCSA) and Spectral Attention (SA) modules, to capture longrange densely spatial interactions and internal spectral similarities. Extensive comparative experiments conducted on three mainstream hyperspectral change detection datasets confirm the effectiveness and superiority of the proposed HyperMatch-based CTSIT in utilizing both labeled and unlabeled samples. For instance, the training ablation demonstrates that this method significantly outperforms most existing state-of-the-art (SOTA) hyperspectral change detection methods, even with a significantly smaller number of samples.

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Section: A Semi-supervised Learningmentioning

confidence: 99%