Results 111 to 120 of about 13,575,612 (179)

A Joint Network of 3D-2D CNN Feature Hierarchy and Pyramidal Residual Model for Hyperspectral Image Classification

IEEE Access
Since convolutional neural networks (CNN) can extract deeper features from hyperspectral images, they show good classification performance in the hyperspectral image (HSI) classification task. However, the performance of many CNN models is constrained by
Hongwei Wei, Yufan Wang, Yu Sun, Jianfeng Zheng, Xiaodong Yu   +4 more
doaj   +1 more source

Multipath Residual Network for Spectral-Spatial Hyperspectral Image Classification

, 2019
Convolutional neural networks (CNNs) have recently shown outstanding capability for hyperspectral image (HSI) classification. In this work, a novel CNN model is proposed, which is wider than other existing deep learning-based HSI classification models ...
Lingling Li, Miaomiao Liang, Zhe Meng, Licheng Jiao, Zhixi Feng, Xu Tang   +5 more
core   +1 more source

AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification

Remote Sensing
Deep learning methods like convolution neural networks (CNNs) and transformers are successfully applied in hyperspectral image (HSI) classification due to their ability to extract local contextual features and explore global dependencies, respectively ...
Xiaofei Yang, Yuxiong Luo, Zhen Zhang, Dong Tang, Zheng Zhou, Haojin Tang   +5 more
doaj   +1 more source

Conventional to Deep Ensemble Methods for Hyperspectral Image Classification: A Comprehensive Survey

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral imaging (HSI) has been widely used in a wide range of real-world application areas due to the in-depth spectral information stored within each pixel. Noticeably, the
Farhan Ullah, Irfan Ullah, R. Khan, Salabat Khan, Khalil Khan, G. Pau   +5 more
semanticscholar   +1 more source

Automatic live and dead cell classification via hyperspectral imaging

, 2019
Classification of live and dead human ovarian cancer cells (SKOV3) is an important task in biomedicine research. Fluorescence is the most common technique to distinguish live and dead cells.
Nguyen, Nam-Trung, Nguyen, NT, Chun-Xia Zhao, Gao, Y, Ho, Benjamin, Jun Zhou, Zhou, Jun, Tan, Say Hwa, Wang, Haofei, Zhao, Chun-Xia, Nam-Trung Nguyen, He Chen, Chen, He, Yongsheng Gao, Zhao, CX, Ho, B, Wang, H, Say Hwa Tan, Haofei Wang, Zhou, J, Benjamin Ho, Chen, H, Tan, SH, Gao, Yongsheng   +23 more
core   +1 more source

Spectral–Spatial Discriminant Feature Learning for Hyperspectral Image Classification

, 2019
Sparse representation classification (SRC) is being widely applied to target detection in hyperspectral images (HSI). However, due to the problem in HSI that high-dimensional data contain redundant information, SRC methods may fail to achieve high ...
Chunhua Dong, Masoud Naghedolfeizi, Dawit Aberra, Xiangyan Zeng   +3 more
core   +1 more source

Few-shot Hyperspectral Image Classification using Relational Generative Adversarial Network

Hyperspectral image (HSI) classification is an essential task in remote sensing, but its performance is greatly affected by limited labeled samples.
Guo, Baoqing, Xiao, Chuangbai, Zhou, Jun, Wei, Wei, Tong, Lei   +4 more
core   +1 more source

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HSI-MFormer: Integrating Mamba and Transformer Experts for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral image (HSI) classification is fundamental to numerous remote sensing applications, enabling detailed analysis of material properties and environmental conditions.
Yan He, Bing Tu, Bo Liu 0020, Jun Li 0009, Antonio Plaza   +4 more
exaly   +4 more sources

HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers

IEEE Transactions on Geoscience and Remote Sensing, 2020
Deep learning methods have been widely used in hyperspectral image classification and have achieved state-of-the-art performance. Nonetheless, the existing deep learning methods are restricted by a limited receptive field, inflexibility, and difficult generalization problems in hyperspectral image classification. To solve these problems, we propose HSI-
Ji He 0003, Lina Zhao 0002, Hongwei Yang, Mengmeng Zhang 0005, Wei Li 0032   +4 more
openaire   +4 more sources

HSI-SSTRANS: Hyperspectral Image Classification with Spectral and Space Transformer

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2023
Daohong Niu, Xiaohua Zhang, Longfei Li, Yuxuan Zhou   +3 more
openaire   +3 more sources