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Deriving Non-Cloud Contaminated Sentinel-2 Images with RGB and Near-Infrared Bands from Sentinel-1 Images Based on a Conditional Generative Adversarial Network

Quan Xiong, Liping Di, Quanlong Feng, Diyou Liu, Wei Liu, Xuli Zan, Lin Zhang, Dehai Zhu, Zhe Liu, Xiaochuang Yao, Xiaodong Zhang
2021 Remote Sensing  
In this paper, we propose a new data fusion model, the Multi-channels Conditional Generative Adversarial Network (MCcGAN), based on the conditional generative adversarial network, which is able to convert  ...  With the model, we were able to generate fused, cloud-free Sentinel-2-like images for a target date by using a pair of reference Sentinel-1/Sentinel-2 images and target-date Sentinel-1 images as inputs  ...  For deriving non-cloud Sentinel-2 images, the cGAN, a supervised GAN, needs a pair of Sentinel-1 and Sentinel-2 images as input.  ... 
doi:10.3390/rs13081512 doaj:5d93d65bd1b94b9eabe305e64b494977 fatcat:2wduvqzm35crxnn6a5alhegfxy

Generative Adversarial Learning in YUV Color Space for Thin Cloud Removal on Satellite Imagery

Xue Wen, Zongxu Pan, Yuxin Hu, Jiayin Liu
2021 Remote Sensing  
The semi-translucent nature of thin clouds provides the possibility of 2D ground scene reconstruction based on a single satellite image.  ...  For the network architecture, a Wasserstein generative adversarial network (WGAN) in YUV color space called YUV-GAN is proposed.  ...  [29] designed multispectral conditional generative adversarial net (McGAN), which extends the input of cGAN by adding a near infrared (NIR) image concatenated with red, green, and blue (RGB) images  ... 
doi:10.3390/rs13061079 fatcat:x2c5uxlzbfamhirgslrmi43yse

Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery

Jun Li, Zhaocong Wu, Zhongwen Hu, Zilong Li, Yisong Wang, Matthieu Molinier
2021 Remote Sensing  
In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR)  ...  CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs.  ...  Acknowledgments: The authors are grateful for the Sentinel-2 data services from the Copernicus Open Access Hub. They also thank J.Z. (Jiaqi Zhang) and G.S. (Guanting Shen) for making the dataset.  ... 
doi:10.3390/rs13010157 fatcat:mi25pwyqqrfrfmc24kownwv7mi

Evaluating Image Normalization via GANs for Environmental Mapping: A Case Study of Lichen Mapping Using High-Resolution Satellite Imagery

Shahab Jozdani, Dongmei Chen, Wenjun Chen, Sylvain G. Leblanc, Julie Lovitt, Liming He, Robert H. Fraser, Brian Alan Johnson
2021 Remote Sensing  
We tested two main scenarios to normalize four target WV2 images to a source 50 cm pansharpened WV2 image: (1) normalizing based only on the WV2 panchromatic band, and (2) normalizing based on the WV2  ...  In this research, by focusing on caribou lichen mapping, we evaluated the potential of using conditional Generative Adversarial Networks (cGANs) for the normalization of WorldView-2 (WV2) images of one  ...  Maas and Rajan [6] proposed a normalization approach (called scatter-plot matching (SPM)) based on invariant features in the scatter plot of pixel values in the red and near-infrared (NIR) bands.  ... 
doi:10.3390/rs13245035 fatcat:t5wwypqbvza6jdpkaxxeq4ynry

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 847-858 Assessment of Agricultural Practices From Sentinel 1 and 2 Images Applied on Rice Fields to Develop a Farm Typology in the Camargue Region.  ...  ., +, JSTARS 2020 5264-5271 Impact of Satellite Sounding Data on Virtual Visible Imagery Generation Using Conditional Generative Adversarial Network.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Rami Al-Ruzouq, Mohamed Barakat A. Gibril, Abdallah Shanableh, Abubakir Kais, Osman Hamed, Saeed Al-Mansoori, Mohamad Ali Khalil
2020 Remote Sensing  
Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions  ...  trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects.  ...  Oil spill incidents recognized from optical images: (a) Sentinel-2 image of ship spills near the coast of Mauritius on 10 August 2020, (b) Sentinel-2 image of massive oil slick off the coast of Kuwait  ... 
doi:10.3390/rs12203338 fatcat:awufdmqg4bhgpi2cmsxy5b52pa

Survey of Deep-Learning Approaches for Remote Sensing Observation Enhancement

Grigorios Tsagkatakis, Anastasia Aidini, Konstantina Fotiadou, Michalis Giannopoulos, Anastasia Pentari, Panagiotis Tsakalides
2019 Sensors  
In this paper, we provide a comprehensive review of deep-learning methods for the enhancement of remote sensing observations, focusing on critical tasks including single and multi-band super-resolution  ...  Deep Learning, and Deep Neural Networks in particular, have established themselves as the new norm in signal and data processing, achieving state-of-the-art performance in image, audio, and natural language  ...  Remote Sensing EO Platforms • Sentinel 2 satellite carrying the 12 band MSI instrument with spatial resolution between 10 and 20 m for most bands in the visible, near and shortwave infrared. • Landsat  ... 
doi:10.3390/s19183929 fatcat:fp7lezjwcfg5fol5hxmgoejg7a

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., and Lopez, J.F  ...  Shadow in Remote Sensing Imagery of Urban Areas With Fine Spatial Resolution Based on Saturation and Near-Infrared Information.  ...  ., +, JSTARS Nov. 2019 4327-4341 A Near-Infrared Band-Based Algorithm for Suspended Sediment Estimation for Turbid Waters Using the Experimental Tiangong 2 Moderate Resolution Wide-Wavelength Imager.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Panagiotis Barmpoutis, Periklis Papaioannou, Kosmas Dimitropoulos, Nikos Grammalidis
2020 Sensors  
Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments  ...  Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire  ...  [68] proposed a smoke detection model using Deeplabv3+ and a generative adversarial network (GAN).  ... 
doi:10.3390/s20226442 pmid:33187292 fatcat:4sw3yywfx5gl5cv3ml6jfkdvja

Table of Contents

2020 IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium  
OF LIDAR-PREDICTED FOREST BIOMASS MAPS FROM RADAR ............................ 4327 BACKSCATTER WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS Sara Björk, Stian Normann Anfinsen, UiT The Arctic University  ...  SENTINEL-1 SAR IMAGES ACCOMPANIED BY AIS IMFORMATION Juyoung Song, Duk-jin Kim, Seoul National University, Korea (South) TH1-R2.4: A TARGET DETECTION ALGORITHM OF NEURAL NETWORK BASED ON .............  ...  FR1-R18: NETWORK BASED CLASSIFIER  ... 
doi:10.1109/igarss39084.2020.9323828 fatcat:6aittajt35gufeaugcmemu5cya

Earth Observation Data-Driven Cropland Soil Monitoring: A Review

Nikolaos Tziolas, Nikolaos Tsakiridis, Sabine Chabrillat, José A. M. Demattê, Eyal Ben-Dor, Asa Gholizadeh, George Zalidis, Bas van Wesemael
2021 Remote Sensing  
This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.  ...  Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds  ...  Acknowledgments: We would like to thank Eleni Kalopesa, for her contribution to the editing and proofreading of the manuscript and for her continuous support and encouragement of this study.  ... 
doi:10.3390/rs13214439 fatcat:k4fowx556zb4jftm6hkccnel3m

Knowledge Extracted from Copernicus Satellite Data

Dumitru Octavian, Schwarz Gottfried, Eltoft Torbjørn, Kræmer Thomas, Wagner Penelope, Hughes Nick, Arthus David, Fleming Andrew, Koubarakis Manolis, Datcu Mihai
2019 Zenodo  
The proposed methodology uses new paradigms from Recurrent Neural Networks and Generative Adversarial Networks, supported by Bayesian and Information Bottleneck concepts. References 1.  ...  By applying an already established active learning approach based on a Support Vector Machine with relevance feedback [2], we can limit ourselves to a limited number of typical satellite images to extract  ...  Automatic glaciers lake mapping, normalized different water index and modify normalized water difference index with on screen digitization (1, 2).  ... 
doi:10.5281/zenodo.3941573 fatcat:zzifwgljifck5bpjnboetsftfu

A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines – From Medical to Remote Sensing [article]

Ankan Dash, Junyi Ye, Guiling Wang
2021 arXiv   pre-print
We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors.  ...  GANs can be used to perform image processing, video generation and prediction, among other computer vision applications.  ...  = 1. 2.8 Image-to-Image Translation with Conditional Adversarial Networks (pix2pix) pix2pix [70] is a conditional generative adversarial network(cGAN [118] ) for solving general purpose image-to-image  ... 
arXiv:2110.01442v1 fatcat:mqpnqw2ysfdz7dneajiw33dbga

Data Fusion in Earth Observation and the Role of Citizen as a Sensor: A Scoping Review of Applications, Methods and Future Trends

Aikaterini Karagiannopoulou, Athanasia Tsertou, Georgios Tsimiklis, Angelos Amditis
2022 Remote Sensing  
Several studies enhanced their methods with the active-, and transfer-learning approaches, constructing a scalable model.  ...  Following this gap of knowledge, we synthesised this scoping systematic literature review (SSLR) with a will to cover this limitation and highlight the benefits and the future directions that remain uncovered  ...  [103] and Olthof and Svacina [105] exploited the Planetscope's spectral bands in visible and near-infrared to identify image patches that could be characterised as flooded.  ... 
doi:10.3390/rs14051263 fatcat:ffg4ulntnrdrxaknuydepyvelm

Proceedings of the World Molecular Imaging Congress 2021, October 5-8, 2021: General Abstracts

2022 Molecular Imaging and Biology  
Conclusions: An efficient, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on patient DICOM images has been used for quantitative improvement in MRI clinical  ...  In general, the values of CNR reached a plateau at around 8 iterations with an average improvement factor of about 1.7 for processed MRI images.  ...  The nanocluster system with a high selectivity showed the potential for fluorescence imaging and the integrating of gold and MMAE demonstrated excellent concurrent chemotherapy-radiotherapy efficacy, which  ... 
doi:10.1007/s11307-021-01693-y pmid:34982365 pmcid:PMC8725635 fatcat:4sfb3isoyfdhfbiwxfr55gvqym
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