Results 1 to 10 of about 4,169 (163)
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image [PDF]
CoRR, 2018 With the development of deep learning, the performance of hyperspectral image (HSI) classification has been greatly improved in recent years. The shortage of training samples has become a bottleneck for further improvement of performance. In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral ...
Xiaosong Zhao
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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 +3 more
doaj +3 more sources
SOFT COMPUTING APPROACHES FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
ICTACT Journal on Soft Computing, 2020 Hyperspectral image classification is one of the most emerging form of image classification. It is able to convey information about an image in a more detailed way as compared to RGB or multispectral data.
H S Prasantha +3 more
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HSI-MSER: Hyperspectral Image Registration Algorithm Based on MSER and SIFT [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 This work was supported in part by Ministerio de Ciencia e Innovación, Government of Spain [grant number PID2019-104834GB-I00], and Consellería de Cultura, Educación e Universidade [grant numbers ED431C 2018/19, and accreditation 2019-2022 ED431G-2019/04]. All are co--funded by the European Regional Development Fund (ERDF).
Álvaro Ordóñez +3 more
openaire +4 more sources
Remote Sensing, 2022 Hyperspectral sharpening has been considered an important topic in many earth observation applications. Many studies have been performed to solve the Visible-Near-Infrared (Vis-NIR) hyperpectral sharpening problem, but there is little research related to
Sihan Huang, David Messinger
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Hyperspectral Super-Resolution Via Joint Regularization of Low-Rank Tensor Decomposition
Remote Sensing, 2021 The hyperspectral image super-resolution (HSI-SR) problem aims at reconstructing the high resolution spatial–spectral information of the scene by fusing low-resolution hyperspectral images (LR-HSI) and the corresponding high-resolution multispectral ...
Meng Cao, Wenxing Bao, Kewen Qu
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Hyperspectral and Multispectral Image Fusion Using Coupled Non-Negative Tucker Tensor Decomposition
Remote Sensing, 2021 Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest.
Marzieh Zare +3 more
doaj +1 more source
How Hyperspectral Image Unmixing and Denoising Can Boost Each Other
Remote Sensing, 2020 Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, the linear model assumed for the HSI in the case of low-rank denoising is often the same as the
Behnood Rasti +3 more
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A Spatial-Spectral Feature Descriptor for Hyperspectral Image Matching
Remote Sensing, 2021 Hyperspectral Images (HSIs) have been utilized in many fields which contain spatial and spectral features of objects simultaneously. Hyperspectral image matching is a fundamental and critical problem in a wide range of HSI applications.
Yang Yu +5 more
doaj +1 more source
Intraoperative reperfusion assessment of human pancreas allografts using hyperspectral imaging (HSI) [PDF]
Hepatobiliary Surgery and Nutrition, 2022 The most common causes of early graft loss in pancreas transplantation are insufficient blood supply and leakage of the intestinal anastomosis. Therefore, it is critical to monitor graft perfusion and oxygenation during the early post-transplant period. The goal of our pilot study was to evaluate the utility of hyperspectral imaging (HSI) in monitoring
Robert, Sucher +9 more
openaire +2 more sources