Results 1 to 10 of about 7,111 (147)

ACTL: Asymmetric Convolutional Transfer Learning for Tree Species Identification Based on Deep Neural Network

IEEE Access, 2021
The identification of tree species is of great significance to the sustainable management and utilization of forest ecosystems. Hyperspectral data provide sufficient spectral and spatial information to classify tree species. Convolutional neural networks
Yun Shi, Donghui Ma, Jie Lv, Jie Li
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Improved Transformer Net for Hyperspectral Image Classification

Remote Sensing, 2021
In recent years, deep learning has been successfully applied to hyperspectral image classification (HSI) problems, with several convolutional neural network (CNN) based models achieving an appealing classification performance.
Yuhao Qing, Wenyi Liu, Liuyan Feng, Wanjia Gao   +3 more
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SquconvNet: Deep Sequencer Convolutional Network for Hyperspectral Image Classification

Remote Sensing, 2023
The application of Transformer in computer vision has had the most significant influence of all the deep learning developments over the past five years.
Bing Li, Qi-Wen Wang, Jia-Hong Liang, En-Ze Zhu, Rong-Qian Zhou   +4 more
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Contrastive Learning Based on Transformer for Hyperspectral Image Classification

Applied Sciences, 2021
Recently, deep learning has achieved breakthroughs in hyperspectral image (HSI) classification. Deep-learning-based classifiers require a large number of labeled samples for training to provide excellent performance.
Xiang Hu, Teng Li, Tong Zhou, Yu Liu, Yuanxi Peng   +4 more
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WHU-OHS: A benchmark dataset for large-scale Hersepctral Image classification

International Journal of Applied Earth Observations and Geoinformation, 2022
Hyperspectral image (HSI) classification is one of the most important remote sensing techniques. Currently, the performances of most of the HSI classification networks on the public HSI datasets are overoptimistic (i.e., the overall accuracy exceeds 98 %)
Jiayi Li, Xin Huang, Lilin Tu
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50: IDENTIFICATION AND CLASSIFICATION OF TUMOR CELLS IN BARRETT’S CARCINOMA PATIENTS BY HYPERSPECTRAL IMAGING (HSI)

Diseases of the Esophagus, 2022
Abstract Background and aim Digital pathology will revolutionize the discriminate of malignant and non-malignant cells in histologically specimens. Hyperspectral imaging (HSI), a new technology combing imaging with spectroscopy might be beneficial for tumor cell identification.
M Maktabi, Y Wichmann, H Köhler, B Jansen-Winkeln, H Ahle, C Chalopin, R Thieme   +6 more
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A semi-supervised cycle-GAN neural network for hyperspectral image classification with minimum noise fraction

Journal of Spectral Imaging, 2022
Hyperspectral imaging (HSI) is a popular mode of remote sensing imaging that collects data beyond the visible spectrum. Many classification techniques have been developed in recent years, since classification is the most crucial task in hyperspectral ...
Tatireddy Subba Reddy, Jonnadula Harikiran   +1 more
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CSR-Net: Camera Spectral Response Network for Dimensionality Reduction and Classification in Hyperspectral Imagery

Remote Sensing, 2020
Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples.
Yunhao Zou, Ying Fu, Yinqiang Zheng, Wei Li   +3 more
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420. IDENTIFICATION AND CLASSIFICATION OF TUMOR CELLS IN PATIENTS WITH BARRETT’S CARCINOMA BY HYPERSPECTRAL IMAGING (HSI)

Diseases of the Esophagus, 2022
Abstract Hyperspectral imaging (HSI), as recently applied in medicine, is a novel technology combining imaging with spectroscopy. It might be used to identify, classify and discriminate malignant and non-malignant cells of histopathologic specimens.
Marianne Maktabi, Yannis Wichmann, Hannes Köhler, Claire Chalopin, Boris Jansen-Winkeln, Ines Gockel, René Thieme   +6 more
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Fusing Spatial Attention with Spectral-Channel Attention Mechanism for Hyperspectral Image Classification via Encoder–Decoder Networks

Remote Sensing, 2022
In recent years, convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification. However, feature extraction on hyperspectral data still faces numerous challenges.
Jun Sun, Junbo Zhang, Xuesong Gao, Mantao Wang, Dinghua Ou, Xiaobo Wu, Dejun Zhang   +6 more
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