Results 141 to 150 of about 13,575,612 (179)
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Journal of Applied Remote Sensing
Self-training-based semi-supervised learning offers an effective means of leveraging large volumes of unlabeled data by iteratively enhancing a model with its own pseudo-label predictions. This paradigm has demonstrated strong potential for reducing annotation costs and improving generalization.
Hongye Xie +4 more
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Self-training-based semi-supervised learning offers an effective means of leveraging large volumes of unlabeled data by iteratively enhancing a model with its own pseudo-label predictions. This paradigm has demonstrated strong potential for reducing annotation costs and improving generalization.
Hongye Xie +4 more
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Single-Source Frequency Transform for Cross-Scene Classification of Hyperspectral Image
IEEE Transactions on Image ProcessingCurrently, the research on cross-scene classification of hyperspectral image (HSI) based on domain generalization (DG) has received wider attention. The majority of the existing methods achieve cross-scene classification of HSI via data manipulation that
Xizeng Huang +3 more
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IEEE Transactions on Geoscience and Remote Sensing
Recently, vision transformer (ViT)-based deep learning (DL) models have achieved remarkable performance gains in hyperspectral image classification (HSIC) due to their abilities to model long-range dependencies and extract global spatial features ...
Zhuo-Yi Zhao +3 more
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Recently, vision transformer (ViT)-based deep learning (DL) models have achieved remarkable performance gains in hyperspectral image classification (HSIC) due to their abilities to model long-range dependencies and extract global spatial features ...
Zhuo-Yi Zhao +3 more
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Neurocomputing
Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural ...
Wei-Lian Zhou +4 more
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Hyperspectral image (HSI) classification is pivotal in the remote sensing (RS) field, particularly with the advancement of deep learning techniques. Sequential models, adapted from the natural language processing (NLP) field such as Recurrent Neural ...
Wei-Lian Zhou +4 more
semanticscholar +1 more source
2019
Extracting biochemical information from cell and tissue autofluorescence without using any external biomarkers is a promising approach for disease diagnosis and treatment. This thesis reports the advancement of fluorescence microscopy and image acquisition tools to observe autofluorescence signals, and I intended to use these signals as a biomarker for
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Extracting biochemical information from cell and tissue autofluorescence without using any external biomarkers is a promising approach for disease diagnosis and treatment. This thesis reports the advancement of fluorescence microscopy and image acquisition tools to observe autofluorescence signals, and I intended to use these signals as a biomarker for
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MASSFormer: Memory-Augmented Spectral-Spatial Transformer for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote SensingIn recent years, convolutional neural networks (CNNs) have achieved remarkable success in hyperspectral image (HSI) classification tasks, primarily due to their outstanding spatial feature extraction capabilities.
Le Sun +5 more
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DBCTNet: Double Branch Convolution-Transformer Network for Hyperspectral Image Classification
IEEE Transactions on Geoscience and Remote SensingCurrently, deep learning (DL) methods represented by convolutional neural networks (CNNs) or Transformers are of great interest in hyperspectral image (HSI) classification.
Rui Xu +5 more
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IEEE Transactions on Geoscience and Remote Sensing
The task of hyperspectral image (HSI) classification is fundamental and crucial in HSI processing. Currently, domain adaptive methods have become a research hotspot in HSI classification.
Jie Feng +6 more
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The task of hyperspectral image (HSI) classification is fundamental and crucial in HSI processing. Currently, domain adaptive methods have become a research hotspot in HSI classification.
Jie Feng +6 more
semanticscholar +1 more source
IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral image (HSI) classification is an essential task in remote sensing with substantial practical significance. However, most existing convolutional neural network (CNN)-based classification methods focus only on local spatial features while ...
Yun-Fei Zhou +4 more
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Hyperspectral image (HSI) classification is an essential task in remote sensing with substantial practical significance. However, most existing convolutional neural network (CNN)-based classification methods focus only on local spatial features while ...
Yun-Fei Zhou +4 more
semanticscholar +1 more source
IEEE Transactions on Geoscience and Remote Sensing
Recently, there have been significant advancements in hyperspectral image (HSI) classification methods employing Transformer architectures. However, these methods, while extracting spectral-spatial features, may introduce irrelevant spatial information ...
Ning Chen +5 more
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Recently, there have been significant advancements in hyperspectral image (HSI) classification methods employing Transformer architectures. However, these methods, while extracting spectral-spatial features, may introduce irrelevant spatial information ...
Ning Chen +5 more
semanticscholar +1 more source