Abstract: In hyperspectral image (HSI) classification, Transformer and CNN are widely used because they complement each other in extracting features. Nevertheless, existing Transformer-based methods ...
Abstract: With the advancement of autonomous driving technologies, passengers increasingly engage in non-driving activities. However, these activities are often limited by motion sickness (MS), which ...
Abstract: This research presents a systematic evaluation of Deep Learning (DL) models for Unmanned Aerial Vehicle (UAV) classification using Range-Doppler Maps (RDMs) under varying noise conditions. A ...
Abstract: In remote sensing (RS), convolutional neural networks (CNNs) are well-recognized for their spatial–spectral feature extraction capabilities, whereas vision transformers (ViTs), which ...
Abstract: Timely and accurate identification of plant diseases is essential for sustainable agricultural practices and food security. This study presents a deep learning-based diagnostic framework ...
Abstract: In recent years, few-shot learning (FSL) has made significant progress in hyperspectral image classification (HSIC) by transferring meta-knowledge from a source domain with sufficient ...
Abstract: Early detection of lung cancer is highly beneficial for patient survival. This paper proposes a hybrid deep learning diagnostic pipeline for pulmonary nodules in chest CT. We constructed a ...
Abstract: In recent years, hyperspectral image classification methods based on convolutional neural networks and Transformer architectures have achieved remarkable success. However, existing ...
Serverless service that generates dynamic Open Graph images that you can embed in your <meta> tags. For each keystroke, headless chromium is used to render an HTML ...
Abstract: Domain adaptation (DA)-based cross-domain hyperspectral image (HSI) classification methods have garnered significant attention. The majority of DA techniques utilize models based on ...
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 ...
Abstract: When facing the challenge of limited samples, existing hyperspectral image (HSI) classification methods typically assume that source domain samples (with prior knowledge) and target task ...
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