However, the existing GCN-based classification methods do not fully utilize the edge relationship, which makes their performance is limited. In addition, a small number of training samples is also a ...

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The effectiveness of spectral-spatial feature learning is crucial for the hyperspectral image (HSI) classification task. Diffusion models, as a new class of groundbreaking generative models, have the ...

Deep learning has attracted wide attention recently because of its excellent feature representation ability and end-to-end automatic learning method. Especially in clinical medical imaging diagnosis, ...

Under the era of technical surge in recent years, the weight of artificial intelligence in people’s life is increasing over time. This paper will focus on classification. How to classify efficiently ...

Early time series classification is of great significance for time-sensitive applications such as fault detection and earthquake prediction. This task aims to classify time series with the least ...

Vision Transformer (ViT), known for capturing non-local features, is an effective tool for hyperspectral image classification (HSIC). However, ViT’s multi-head self-attention (MHSA) mechanism often ...

In hyperspectral images (HSIs), different land cover (LC) classes have distinct reflective characteristics at various wavelengths. Therefore, relying on only a few bands to distinguish all LC classes ...

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Lois Kathryn Earl Dewey, our beloved mother, grandmother, sister, and friend, passed away peacefully on Tuesday, June 24th. She was born on September 12, 1930 to Roy Reed and Katie Sorenson Earl in ...

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Classification of hyperspectral data with high spatial resolution from urban areas is investigated. A method based on mathematical morphology for preprocessing of the hyperspectral data is proposed.