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Found 21 papers, 2 papers with code
Adaptive Regularized Low-Rank Tensor Decomposition for Hyperspectral Image Denoising and Destriping
On the one hand, the stripe noise is separately modeled by the tensor decomposition, which can effectively encode the spatial-spectral correlation of the stripe noise.
A physics-constrained machine learning method for mapping gapless land surface temperature
Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model.
An attention mechanism based convolutional network for satellite precipitation downscaling over China
Precipitation is a key part of hydrological circulation and is a sensitive indicator of climate change.
Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data
In this paper, we present an integrated temperature fusion framework for satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m spatial resolution and half-hourly temporal resolution.
An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images
Images from many remote sensing satellites, including MODIS, Landsat-8, Sentinel-1, and Sentinel-2, are utilized in the experiments.
Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion
In this paper, we systematically investigate the coupling of model-driven and data-driven methods, which has rarely been considered in the remote sensing image restoration and fusion communities.
Fully Polarimetric SAR and Single-Polarization SAR Image Fusion Network
The data fusion technology aims to aggregate the characteristics of different data and obtain products with multiple data advantages.
A modified two-leaf light use efficiency model for improving the simulation of GPP using a radiation scalar
The same maximum LUE is used for both sunlit and shaded leaves, and the difference in LUE between sunlit and shaded leaf groups is determined by the same radiation scalar.
Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion
The applications of Normalized Difference Vegetation Index (NDVI) time-series data are inevitably hampered by cloud-induced gaps and noise.
Spectral Response Function Guided Deep Optimization-driven Network for Spectral Super-resolution
Hyperspectral images are crucial for many research works.
Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors
In this paper, we propose a deep learning based cloud detection method named multi-scale convolutional feature fusion (MSCFF) for remote sensing images of different sensors.
Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network
The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs applications.
Oblique Stripe Removal in Remote Sensing Images via Oriented Variation
Destriping is a classical problem in remote sensing image processing.
Spatial-Spectral Fusion by Combining Deep Learning and Variation Model
In the field of spatial-spectral fusion, the model-based method and the deep learning (DL)-based method are state-of-the-art.
Land-Cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models
The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models.
Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications.
A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multi-spectral (MS) images.
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
Remote sensing images often suffer from cloud cover.
Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network
In the field of fusing multi-spectral and panchromatic images (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the drawbacks of traditional linear models and boost the fusing accuracy.
A Spatial and Temporal Non-Local Filter Based Data Fusion
The spatiotemporal data fusion technique is considered as a cost-effective way to obtain remote sensing data with both high spatial resolution and high temporal frequency, by blending observations from multiple sensors with different advantages or characteristics.
Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery
In this paper, an automatic multi-feature combined (MFC) method is proposed for cloud and cloud shadow detection in GF-1 WFV imagery.
