Results 11 to 20 of about 1,639 (207)

Self-Supervised Denoising for Real Satellite Hyperspectral Imagery

Remote Sensing, 2022
Satellite hyperspectral remote sensing has gradually become an important means of Earth observation, but the existence of various types of noise seriously limits the application value of satellite hyperspectral images.
Jinchun Qin, Hongrui Zhao, Bing Liu
doaj   +2 more sources

HYPERSPECTRAL IMAGE DENOISING WITH CUBIC TOTAL VARIATION MODEL [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012
Image noise is generated unavoidably in the hyperspectral image acquision process and has a negative effect on subsequent image analysis. Therefore, it is necessary to perform image denoising for hyperspectral images.
H. Zhang
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Spatial-Spectral Transformer for Hyperspectral Image Denoising

Proceedings of the AAAI Conference on Artificial Intelligence, 2023
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications. Unfortunately, though witnessing the development of deep learning in HSI denoising area, existing convolution-based methods face the trade-off between computational efficiency and capability to model non-local characteristics of HSI.
Miaoyu Li, Ying Fu 0001, Yulun Zhang 0001   +2 more
openaire   +3 more sources

Deep Parameterized Neural Networks for Hyperspectral Image Denoising

IEEE Transactions on Geoscience and Remote Sensing, 2023
Sparse representation (SR)-based hyperspectral image (HSI) denoising methods normally average the local denoising results of multiple overlapped cubes to recover the whole HSI. Though interpretable, they rely on cumbersome hyperparameter settings and ignore the relationship between overlapped cubes, leading to poor denoising performance.
Fengchao Xiong, Jun Zhou 0001, Jiantao Zhou 0001, Jianfeng Lu 0003, Yuntao Qian   +4 more
openaire   +3 more sources

Hyperspectral Denoising Using Asymmetric Noise Modeling Deep Image Prior

Remote Sensing, 2023
Deep image prior (DIP) is a powerful technique for image restoration that leverages an untrained network as a handcrafted prior. DIP can also be used for hyperspectral image (HSI) denoising tasks and has achieved impressive performance.
Yifan Wang, Shuang Xu, Xiangyong Cao, Qiao Ke, Teng-Yu Ji, Xiangxiang Zhu   +5 more
doaj   +2 more sources

Hyperspectral Image Destriping and Denoising Using Stripe and Spectral Low-Rank Matrix Recovery and Global Spatial-Spectral Total Variation

Remote Sensing, 2021
Hyperspectral image (HSI) is easily corrupted by different kinds of noise, such as stripes, dead pixels, impulse noise, Gaussian noise, etc. Due to less consideration of the structural specificity of stripes, many existing HSI denoising methods cannot ...
Fang Yang, Xin Chen, Li Chai
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Soil Organic Matter Prediction Model with Satellite Hyperspectral Image Based on Optimized Denoising Method

Remote Sensing, 2021
In order to improve the signal-to-noise ratio of the hyperspectral sensors and exploit the potential of satellite hyperspectral data for predicting soil properties, we took MingShui County as the study area, which the study area is approximately 1481 km2,
Xiangtian Meng, Yilin Bao, Qiang Ye, Huanjun Liu, Xinle Zhang, Haitao Tang, Xiaohan Zhang   +6 more
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Iterative Low-Rank Network for Hyperspectral Image Denoising

IEEE Transactions on Geoscience and Remote Sensing
Hyperspectral image (HSI) denoising is a crucial preprocessing step for subsequent tasks. The clean HSI usually reside in a low-dimensional subspace, which can be captured by low-rank and sparse representation, known as the physical prior of HSI. It is generally challenging to adequately use such physical properties for effective denoising while ...
Jin Ye 0008, Fengchao Xiong, Jun Zhou 0001, Yuntao Qian   +3 more
openaire   +4 more sources

Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral Image Denoising

Remote Sensing, 2023
Hyperspectral image (HSIs) denoising is a preprocessing step that plays a crucial role in many applications used in Earth observation missions. Low-rank tensor representation can be utilized to restore mixed-noise HSIs, such as those affected by mixed ...
Jie Han, Chuang Pan, Haiyong Ding, Zhichao Zhang   +3 more
doaj   +2 more sources

SSIT: A spatial–spectral interactive transformer for hyperspectral image denoising

Science of Remote Sensing
Convolutional neural networks have been successfully applied to hyperspectral image denoising, but they cannot effectively capture global information in the image.
Ziyu Chen, Changhong Liu, Jun Zhou
doaj   +2 more sources