Hyperspectral image (HSI) data have a wide range of spectral information that is valuable for numerous tasks. HSI data encounter some challenges, like insufficient representation of spectral spatial ...

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

Traditional supervised deep learning (DL) methods for hyperspectral image (HSI) classification are severely limited by the quality and quantity of labels. Furthermore, existing feature extraction ...

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 modalities by ...

CNN is a successful image classification that uses hierarchical feature extraction, ViTs capture the global context but require substantial data and computation. In this research, we have used ...

In recent years, graph convolutional networks (GCNs) have been introduced for hyperspectral image (HSI) classification due to their ability to effectively process the inherent graph structure of HSI ...

Discover the fascinating world of DIY science projects with our step-by-step guide on how to make a vacuum cleaner using a plastic bottle. In this engaging tutorial, you'll learn how to transform ...

Domain adaptation (DA)-based cross-domain hyperspectral image (HSI) classification methods have garnered significant attention. The majority of DA techniques utilize models based on convolutional ...

To obtain light ensemble model through clearly explained effective ensemble member selection and finding data representation in various valuable forms are major challenges in medical image ...

Vision transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performance in classifying complicated hyperspectral images (HSIs). However, these models require a lot ...

Traditional brain tumor diagnosis and classification are time-consuming and heavily reliant on radiologist expertise. The ever-growing patient population generates vast data, rendering existing ...