Results 1 to 10 of about 763 (135)

A Dual-Branch Extraction and Classification Method Under Limited Samples of Hyperspectral Images Based on Deep Learning

Remote Sensing, 2020
The convolutional neural network (CNN) has been gradually applied to the hyperspectral images (HSIs) classification, but the lack of training samples caused by the difficulty of HSIs sample marking and ignoring of correlation between spatial and spectral
Bingqing Niu, Jinhui Lan, Yang Shao, Hui Zhang   +3 more
doaj   +3 more sources

HyperHazeOff: Hyperspectral Remote Sensing Image Dehazing Benchmark [PDF]

Journal of Imaging
Hyperspectral remote sensing images (HSIs) provide invaluable information for environmental and agricultural monitoring, yet they are often degraded by atmospheric haze, which distorts spatial and spectral content and hinders downstream analysis ...
Artem Nikonorov, Dmitry Sidorchuk, Nikita Odinets, Vladislav Volkov, Anastasia Sarycheva, Ekaterina Dudenko, Mikhail Zhidkov, Dmitry Nikolaev   +7 more
doaj   +2 more sources

A Hyperspectral Remote Sensing Image Encryption Algorithm Based on a Novel Two-Dimensional Hyperchaotic Map [PDF]

Entropy
With the rapid advancement of hyperspectral remote sensing technology, the security of hyperspectral images (HSIs) has become a critical concern. However, traditional image encryption methods—designed primarily for grayscale or RGB images—fail to address
Zongyue Bai, Qingzhan Zhao, Wenzhong Tian, Xuewen Wang, Jingyang Li, Yuzhen Wu   +5 more
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Evaluation of Focus Measures for Hyperspectral Imaging Microscopy Using Principal Component Analysis [PDF]

Journal of Imaging
An automatic focusing system is a crucial component of automated microscopes, adjusting the lens-to-object distance to find the optimal focus by maximizing the focus measure (FM) value.
Humbat Nasibov
doaj   +2 more sources

Synthesis of Synthetic Hyperspectral Images with Controllable Spectral Variability Using a Generative Adversarial Network

Remote Sensing, 2023
In hyperspectral unmixing (HU), spectral variability in hyperspectral images (HSIs) is a major challenge which has received a lot of attention over the last few years.
Burkni Palsson, Magnus O. Ulfarsson, Johannes R. Sveinsson   +2 more
doaj   +1 more source

Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior

Remote Sensing, 2021
The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements.
Lina Zhuang, Michael K. Ng, Xiyou Fu
doaj   +1 more source

Novel Data-Driven Spatial-Spectral Correlated Scheme for Dimensionality Reduction of Hyperspectral Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
Hyperspectral imaging technology has been popularly applied in remote sensing because it collects echoed signals from across the electromagnetic (EM) spectrum and thereby contributes fruitfully spatial-spectral information.
Yanming Zhang, Ping Yuan, Lijun Jiang, Hong Tat Ewe   +3 more
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Thangka Hyperspectral Image Super-Resolution Based on a Spatial–Spectral Integration Network

Remote Sensing, 2023
Thangka refers to a form of Tibetan Buddhist painting on a fabric, scroll, or Thangka, often depicting deities, scenes, or mandalas. Deep-learning-based super-resolution techniques have been applied to improve the spatial resolution of hyperspectral ...
Sai Wang, Fenglei Fan
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A NEW SPECTRAL-SPATIAL SUBSPACE CLUSTERING ALGORITHM FOR HYPERSPECTRAL IMAGE ANALYSIS [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020
In the past decade, hyperspectral imaging techniques have been widely used in various applications to acquire high spectral-spatial resolution images from different objects and materials.
K. Rafiezadeh Shahi, P. Ghamisi, R. Jackisch, M. Khodadadzadeh, S. Lorenz, R. Gloaguen   +5 more
doaj   +1 more source

Linear Spatial Misregistration Detection and Correction Based on Spectral Unmixing for FAHI Hyperspectral Imagery

Sensors, 2022
In push-broom hyperspectral imaging systems, the sensor rotation to the optical plane leads to linear spatial misregistration (LSM) in hyperspectral images (HSIs).
Xiangyue Zhang, Xiaoyu Cheng, Tianru Xue, Yueming Wang   +3 more
doaj   +1 more source