Results 301 to 310 of about 153,077 (360)

Enhanced Deep Image Prior for Unsupervised Hyperspectral Image Super-Resolution

IEEE Transactions on Geoscience and Remote Sensing
Depending on a large-scale paired dataset of low-resolution hyperspectral image (LrHSI), high-resolution multispectral image (HrMSI), and corresponding high-resolution hyperspectral image (HrHSI), the supervised paradigm has achieved impressive ...
Jiaxin Li, Ke Zheng, Lianru Gao, Zhu Han, Zhi Li, Jocelyn Chanussot   +5 more
semanticscholar   +1 more source

Compressive Hyperspectral Imaging

Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP), 2023
Conventional hyperspectral cameras encounter a trade-off between spatial and spectral samplings while capturing an input scene. To address this problem, we propose two imaging systems: Hyperspectral Light Field Tomography (Hyper-LIFT) and Tunable Image Projection Spectrometry (TIPS).
Qi Cui, Liang Gao
openaire   +1 more source

UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
In recent years, significant advances in unmanned aerial vehicle (UAV) technology and hyperspectral remote sensing have spurred rapid and innovative developments in UAV-based hyperspectral image (HSI) classification across a range of fields, including ...
Zhen Zhang, Lehao Huang, Qingwang Wang, Linhuan Jiang, Yemao Qi, Shunyuan Wang, Tao Shen, Bo-Hui Tang, Yanfeng Gu   +8 more
semanticscholar   +1 more source

Deep Recurrent Neural Networks for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2017
Lichao Mou, Pedram Ghamisi, Xiao Xiang Zhu   +2 more
exaly   +2 more sources

Model-Informed Multistage Unsupervised Network for Hyperspectral Image Super-Resolution

IEEE Transactions on Geoscience and Remote Sensing
By fusing a low-resolution hyperspectral image (LrMSI) with an auxiliary high-resolution multispectral image (HrMSI), hyperspectral image super-resolution (HISR) can generate a high-resolution hyperspectral image (HrHSI) economically.
Jiaxin Li, Ke Zheng, Lianru Gao, Li Ni, Min Huang, Jocelyn Chanussot   +5 more
semanticscholar   +1 more source

Hyperspectral Image Classification Using Groupwise Separable Convolutional Vision Transformer Network

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 ...
Zhuoyi Zhao, Xiang Xu, Shutao Li, A. Plaza   +3 more
semanticscholar   +1 more source

A review of deep learning used in the hyperspectral image analysis for agriculture

Artificial Intelligence Review, 2021
Yanjun Zhu, Jialin Hou, Ping Liu
exaly   +2 more sources

Compression of hyperspectral imagery

Data Compression Conference, 2003. Proceedings. DCC 2003, 2003
High dimensional source vectors, such as those that occur in hyperspectral imagery, are partitioned into a number of subvectors of different length and then each subvector is vector quantized (VQ) individually with an appropriate codebook. A locally adaptive partitioning algorithm is introduced that performs comparably in this application to a more ...
Giovanni Motta, Francesco Rizzo, James A. Storer   +2 more
openaire   +1 more source

On Hyperspectral Super-Resolution

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021
In this paper we will review seminal contributions of Prof. Jose Bioucas Dias for the improvement of the spatial resolution of hyperspectral images. Be it through the extension of pansharpening algorithms with spatial and spectral sparsity priors, using spectral unmixing, using a low-rank assumption from complementary multisource data, or by designing ...
openaire   +1 more source

Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing, 2020
Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked hundreds of contiguous narrowbands. Due to the existence of noise and band correlation, the selection of informative spectral–spatial kernel features poses a challenge ...
S. K. Roy, Suvojit Manna, Tiecheng Song, L. Bruzzone   +3 more
semanticscholar   +1 more source