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A Quadratic Morphological Deep Neural Network Fusing Radar and Optical Data for the Mapping of Burned Areas

Seyd Teymoor Seydi, Mahdi Hasanlou, Jocelyn Chanussot
2022 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
QMDNN-Net has double streams for extracting deep features from multitemporal coherence data and Sentinel-2 imagery.  ...  To this end, this study presents a novel burned-area mapping framework based on the fusion of multitemporal Sentinel-1 coherence imagery and post-event Sentinel-2 imagery.  ...  pixels; and 2) use of a contextual filter to refine the classified pixels based on VIIRS I-band active fire detection.  ... 
doi:10.1109/jstars.2022.3175452 fatcat:ci5g3aujevbrndt6mnuwhlgna4

Active Fire Detection in Landsat-8 Imagery: a Large-Scale Dataset and a Deep-Learning Study [article]

Gabriel Henrique de Almeida Pereira and André Minoro Fusioka and Bogdan Tomoyuki Nassu and Rodrigo Minetto
2021 arXiv   pre-print
This paper addresses these issues by introducing a new large-scale dataset for active fire detection, with over 150,000 image patches (more than 200 GB of data) extracted from Landsat-8 images captured  ...  In this paper, we address the problem of active fire detection using deep learning techniques.  ...  Acknowledgment We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
arXiv:2101.03409v1 fatcat:2oh7adnsfjfxze3pdvhruln6pq

Wildfire Segmentation Using Deep Vision Transformers

Rafik Ghali, Moulay A. Akhloufi, Marwa Jmal, Wided Souidene Mseddi, Rabah Attia
2021 Remote Sensing  
Techniques based on Convolutional Networks are the most used and have proven to be efficient at solving such a problem.  ...  Thus, we design two frameworks based on the former image Transformers adapted to our complex, non-structured environment, which we evaluate using varying backbones and we optimize for forest fires' segmentation  ...  Through the years, many studies addressed the problem of fire detection based on deep learning techniques. For instance, Gonzalez et al.  ... 
doi:10.3390/rs13173527 doaj:5c61c0466ae44324bd2a6dfba393be08 fatcat:75wbzuhswba6dn2ry4jxxwmeqi

Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

Xikun Hu, Yifang Ban, Andrea Nascetti
2021 Remote Sensing  
Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than  ...  Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard  ...  We acknowledge the use of data from Sentinel-2 operated by the Copernicus Programme and Landsat-8 data by NASA.  ... 
doi:10.3390/rs13081509 doaj:fa579a4d3ad346a2b492b2448310403c fatcat:jhls5zm62bchzkhtb4srrjr2xy

Single-Image Super-Resolution of Sentinel-2 Low Resolution Bands with Residual Dense Convolutional Neural Networks

Luis Salgueiro, Javier Marcello, Verónica Vilaplana
2021 Remote Sensing  
In this work, we propose a single-image super-resolution model based on convolutional neural networks that enhances the low-resolution bands (20 m and 60 m) to reach the maximal resolution sensed (10 m  ...  Sentinel-2 satellites have become one of the main resources for Earth observation images because they are free of charge, have a great spatial coverage and high temporal revisit.  ...  [7] adapted a Super-Resolution CNN (SRCNN) [30] to work with the HR and LR bands of Sentinel-2. Wagner et al.  ... 
doi:10.3390/rs13245007 fatcat:h4c2cm26cffn7cehnpy2lpcjuq

Active Fire Detection Using a Novel Convolutional Neural Network Based on Himawari-8 Satellite Images

Zhonghua Hong, Zhizhou Tang, Haiyan Pan, Yuewei Zhang, Zhongsheng Zheng, Ruyan Zhou, Zhenling Ma, Yun Zhang, Yanling Han, Jing Wang, Shuhu Yang
2022 Frontiers in Environmental Science  
Therefore, in this research we proposed an active fire detection system using a novel convolutional neural network (FireCNN).  ...  The proposed method was tested on dataset which contained 1,823 fire spots and 3,646 non-fire spots.  ...  ACKNOWLEDGMENTS Many thanks to the reviewers for their valuable comments.  ... 
doi:10.3389/fenvs.2022.794028 fatcat:6rbfbydgezcqjhnljvhkswpxky

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

Panagiotis Barmpoutis, Periklis Papaioannou, Kosmas Dimitropoulos, Nikos Grammalidis
2020 Sensors  
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  ...  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  ...  A multi-temporal change-detection technique, namely robust satellite techniques for fires detection and monitoring (RST-FIRES) using data from the MSG-SEVIRI sensor. Filizzola et al.  ... 
doi:10.3390/s20226442 pmid:33187292 fatcat:4sw3yywfx5gl5cv3ml6jfkdvja

Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training

Darren Pouliot, Rasim Latifovic, Jon Pasher, Jason Duffe
2018 Remote Sensing  
In this research we test shallow and deep convolution neural networks (CNNs) for Landsat image super-resolution enhancement, trained using Sentinel-2, in three study sites representing boreal forest, tundra  ...  Landsat is a fundamental data source for understanding historical change and its effect on environmental processes.  ...  Acknowledgments: This research was supported through a Canadian Space Agency grant for the project "Integrated Earth Observation Monitoring for Essential Ecosystem Information: Resilience to Ecosystem  ... 
doi:10.3390/rs10030394 fatcat:s7zzh5rakrdh3daahtjp37pll4

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  
CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree.  ...  ., +, JSTARS 2020 227-240 Satellite-Based Fire Progression Mapping: A Comprehensive Assessment for Large Fires in Northern California.  ...  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

2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14

2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  of Active Wild-fires.  ...  Data Based on Machine Learning Technique.  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

Remote Sensing Big Data Classification with High Performance Distributed Deep Learning

Rocco Sedona, Gabriele Cavallaro, Jenia Jitsev, Alexandre Strube, Morris Riedel, Jón Atli Benediktsson
2019 Remote Sensing  
This paper shows that the training of state-of-the-art deep Convolutional Neural Networks (CNNs) can be efficiently performed in distributed fashion using parallel implementation techniques on HPC machines  ...  containing a large number of Graphics Processing Units (GPUs).  ...  One is based on the super-resolution deep network approach proposed by Lanaras et al. in [56] . Using super-resolved images, we can obtain the same high resolution across different bands.  ... 
doi:10.3390/rs11243056 fatcat:fxfemqy6nnd2lo2ukgh34cevea

Object Tracking Based on Satellite Videos: A Literature Review

Zhaoxiang Zhang, Chenghang Wang, Jianing Song, Yuelei Xu
2022 Remote Sensing  
Finally, a revised multi-level dataset based on wpafb videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area.  ...  However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution.  ...  Acknowledgments: We thank the anonymous reviewers and editors for their constructive comments and suggestions, which helped us to improve the manuscript.  ... 
doi:10.3390/rs14153674 fatcat:fhpk7dx6iba55msd3o2kaxppa4

Table of Contents

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Torres 4399 Multispectral Data Analysis for Semantic Assessment-A SNAP Framework for Sentinel-2 Use Case Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. C. Grivei, I. C.  ...  Barbier, and A. Orban 4085 Change Detection in Unlabeled Optical Remote Sensing Data Using Siamese CNN . and Z.  ...  Pan 4518 CDL: A Cloud Detection Algorithm Over Land for MWHS-2 Based on the Gradient Boosting Decision Tree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/jstars.2020.3046663 fatcat:zqzyhnzacjfdjeejvzokfy4qze

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  
Carreno-Luengo, H., +, JSTARS Jan. 2019 107-122 Soft-Then-Hard Super-Resolution Mapping Based on Pansharpening Technique for Remote Sensing Image.  ...  ., +, JSTARS Aug. 2019 2934-2943 Developing an Automatic Phenology-Based Algorithm for Rice Detection Using Sentinel-2 Time-Series Data.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

Integrating Deep Learning and Augmented Reality to Enhance Situational Awareness in Firefighting Environments [article]

Manish Bhattarai
2021 arXiv   pre-print
Third, we built a deep Q-learning-based agent, immune to stress-induced disorientation and anxiety, capable of making clear navigation decisions based on the observed and stored facts in live-fire environments  ...  Finally, we used a low computational unsupervised learning technique called tensor decomposition to perform meaningful feature extraction for anomaly detection in real-time.  ...  to generate the per-pixel based segmentation mask for the object. [184] first uses a super-resolution method to increase the target signature resolution followed by a faster RCNN-based CNN framework  ... 
arXiv:2107.11043v2 fatcat:3jm5zawelze7dhx37luja7mly4
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