Abstract: Convolutional neural network (CNN) and transformer-based hybrid models have been successfully applied to hyperspectral image (HSI) classification, enhancing the local feature extraction ...

Abstract: Vitamin deficiency is a widespread global health issue that affects millions, often leading to severe physiological and dermatological complications. Early detection is essential for timely ...

Abstract: Hyperspectral image (HSI) classification suffers from severe catastrophic forgetting in exemplar-free lifelong learning, where models must continuously learn new land-cover categories ...

Abstract: Hyperspectral image (HSI) classification models suffer from a phenomenon known as catastrophic forgetting, which refers to the sharp decline in performance on previously learned tasks after ...

Abstract: Recently, convolutional neural networks (CNNs) and Transformers have achieved considerable success in hyperspectral image (HSI) classification tasks. However, existing methods not only lack ...

Abstract: Vision Transformer (ViT) has been thoroughly explored in hyperspectral image (HSI) classification (HIC). Nevertheless, current ViT-based approaches still acquire discriminative features, ...

Abstract: Domain adaptation methods enable model migration and adaptation across different domain data distributions. However, the source and target domains of hyperspectral images (HSIs) have large ...

Iowa Democrats secured a consequential victory in a special election on Tuesday, CNN projects, breaking the Republican supermajority in the state Senate by flipping a seat in a district President ...

(CNN) — Democrat Catelin Drey will win an Iowa state Senate special election, CNN projects, flipping the seat and breaking the Republican supermajority in the chamber. President Donald Trump won the ...

Abstract: In this article, a multimodal deep architecture for classification of light detection and ranging (LiDAR) and hyperspectral image (HSI) is proposed, acquiring the knowledge of both ...

Abstract: This study aims to develop a novel deep learningbased approach to support the automated mushroom growth monitoring using an object tracking algorithm in conjunction with instance ...