Abstract: Hyperspectral image classification (HSIC) is a critical task in remote sensing. However, the performance of deep-learning-based HSIC methods degrades significantly when training data ...
Abstract: Hyperspectral image (HSI) classification is a critical task in remote sensing. Recently, deep learning (DL) methods, particularly state space models (SSMs) like Mamba, have garnered ...
Abstract: One of the key challenges in cross-domain few-shot hyperspectral image classification (HSIC) lies in effectively leveraging spectral-spatial features while alleviating semantic ...
Abstract: Accurate classification of brain tumors from magnetic resonance imaging (MRI) scans is essential for early diagnosis and reliable clinical decision-making. However, variations in tumor ...
Abstract: Various deep learning-based methods have greatly improved hyperspectral image (HSI) classification performance, but these models are sensitive to noisy training labels. Human annotation on ...
Abstract: While attention-based approaches have shown considerable progress in enhancing image fusion and addressing the challenges posed by long-range feature dependencies, their efficacy in ...
Abstract: Convolutional Neural Networks (CNNs) excel in local feature extraction but struggle to model regional semantic correlations and global context. This paper proposes a GNNintegrated framework ...
Abstract: Hyperspectral image classification is a crucial research area in hyperspectral remote sensing, aiming to identify the land cover type of each pixel in remote sensing images using computer ...
Abstract: Fine-grained image classification (FGIC) remains a challenging task due to subtle inter-class differences and significant intra-class variations, particularly under limited training data.
Abstract: Skin cancer ranks among ubiquitous malignancies, its prevalence escalating due to ecological shifts and protracted ultraviolet (UV)exposure. This study aims to address the pressing need for ...
Abstract: In the field of agriculture, plant diseases pose a serious threat to achieving optimal yields and food security; thus, identifying and classifying rice leaf diseases correctly are key points ...
Abstract: This paper proposes the classification of mushrooms using the EfficientNetB7 deep learning architecture. Correct classification of mushrooms is important because most species are considered ...
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