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Deep learning for semantic segmentation of remote sensing images with rich spectral content

A. Ben Hamida, A. Benoit, P. Lambert, L. Klein, C. Ben Amar, N. Audebert, S. Lefevre
2017 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
Therefore, this paper presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation.  ...  Among popular techniques in remote sensing, Deep Learning gains increasing interest but depends on the quality of the training data.  ...  [4] benchmarked different machine learning methods (including multilayered perceptron) for classification of Sentinel-2 images.  ... 
doi:10.1109/igarss.2017.8127520 dblp:conf/igarss/HamidaBLKAAL17 fatcat:52dxfdt4vba7pjfvi7572sr46y

Deep learning for semantic segmentation of remote sensing images with rich spectral content [article]

A Hamida, A. Benoît, L Klein, C Amar, N. Audebert
2017 arXiv   pre-print
Therefore, this paper presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation.  ...  Among popular techniques in remote sensing, Deep Learning gains increasing interest but depends on the quality of the training data.  ...  [4] benchmarked different machine learning methods (including multilayered perceptron) for classification of Sentinel-2 images.  ... 
arXiv:1712.01600v1 fatcat:46njo5nqpvhkrh5rytkv7odede

UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions

Ana I. de Castro, Yeyin Shi, Joe Mari Maja, Jose M. Peña
2021 Remote Sensing  
Some investigations focused on issues related to UAV flight operations, spatial resolution requirements, and computation and data analytics, while others studied the ability of UAVs for characterizing  ...  This paper reviewed a set of twenty-one original and innovative papers included in a special issue on UAVs for vegetation monitoring, which proposed new methods and techniques applied to diverse agricultural  ...  Finally, the research applied the thermal sensors mainly for water stress measures, aiming to develop efficient irrigation systems by optimizing water use at the crop field scale.  ... 
doi:10.3390/rs13112139 fatcat:zbirrq37cjgxbpo5zooplm2jra

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

Ali Jamali
2019 SN Applied Sciences  
Among the eight machine learning algorithms used for image classification based on the training and test dataset, NN ge classifier is ranked first with values of 100, 0, and 0 for Overall Accuracy, Mean  ...  Firstly, Landsat 8 OLI/TIRS Level-2 images based on eight machine learning techniques including Random Forest, Decision Table, DTNB, J48, Lazy IBK, Multilayer Perceptron, Non-Nested Generalized Exemplars  ...  Land use land cover maps To provide a reliable estimation of the environmental assessment using Landsat 8 OLI for the city of Sari, the outputs of eight different machine learning classification techniques  ... 
doi:10.1007/s42452-019-1527-8 fatcat:glsrqyfjdbbiro4eshfob4m6rq

Estimating and Examining the Sensitivity of Different Vegetation Indices to Fractions of Vegetation Cover at Different Scaling Grids for Early Stage Acacia Plantation Forests Using a Fixed-Wing UAS

Kotaro Iizuka, Tsuyoshi Kato, Sisva Silsigia, Alifia Yuni Soufiningrum, Osamu Kozan
2019 Remote Sensing  
First, the UAS was utilized to collect high-resolution RGB imagery and multispectral images for the study area.  ...  This study indicates that UAS-based FVC estimations can be used for observing fast-growing acacia trees at a fine scale resolution, which may assist current restoration programs in Indonesia.  ...  Mayangkara Tanaman Industri (MTI) for all the support during the field work. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs11151816 fatcat:5suv73ehhbd4hif7ox6qsohi44

Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

Martin Engen, Erik Sandø, Benjamin Lucas Oscar Sjølander, Simon Arenberg, Rashmi Gupta, Morten Goodwin
2021 Agronomy  
Recent studies on crop yield production are limited to regional-scale predictions.  ...  For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets.  ...  Acknowledgments: We would like to thank the Faculty of Engineering and Science and the CAIR Research lab at the University of Agder, Norway, for allowing us to conduct the research on this topic.  ... 
doi:10.3390/agronomy11122576 fatcat:32ccu6xccvgkfknmxll45lblgy

Big Data and Machine Learning with Hyperspectral Information in Agriculture

Kenneth Li-minn Ang, Jasmine Kah Phooi Seng
2021 IEEE Access  
To the best of our knowledge, no similar review study on agriculture with Big data, machine learning and deep learning for hyperspectral and multispectral information processing has been reported.  ...  The potential for utilizing Big data, machine learning and deep learning for hyperspectral and multispectral data in agriculture is very promising.  ...  The authors in [8] presented a review on the utilization and deployment of Big data analysis in agriculture. The authors in [3] focused on Big data and machine learning for crop protection.  ... 
doi:10.1109/access.2021.3051196 fatcat:hewivbzua5a27jotazlmqvps7i

A Review on the Use of Unmanned Aerial Vehicles and Imaging Sensors for Monitoring and Assessing Plant Stresses

Jayme Garcia Arnal Barbedo
2019 Drones  
Indeed, the use of UAVs for monitoring and assessing crops, orchards, and forests has been growing steadily during the last decade, especially for the management of stresses such as water, diseases, nutrition  ...  This article presents a critical overview of the main advancements on the subject, focusing on the strategies that have been used to extract the information contained in the images captured during the  ...  A related variable, called Non Water Stress Baseline (NWSB), was also used in some investigations [61] .  ... 
doi:10.3390/drones3020040 fatcat:33jjpgn6rnanxgrnu7235ooymy

Identification of raining clouds using a method based on optical and microphysical cloud properties from Meteosat second generation daytime and nighttime data

Mourad Lazri, Soltane Ameur, Jean Michel Brucker, Jacques Testud, Bachir Hamadache, Slimane Hameg, Fethi Ouallouche, Yacine Mohia
2013 Applied Water Science  
The algorithm is calibrated by instantaneous meteorological radar using multilayer perceptron. Radar provided the "ground precipitation truth" for training and validation.  ...  A new scheme for the delineation of raining and non-raining cloud areas applicable to mid-latitudes from daytime and nighttime multispectral satellite data is developed.  ...  Therefore, a two-layer perceptron network can be used for the rainfall estimation problem.  ... 
doi:10.1007/s13201-013-0079-0 fatcat:b3l7pxmoijfthekrvi6uf6jqmm

Multi-sensor data fusion for urban area classification

Aliaksei Makarau, Gintautas Palubinskas, Peter Reinartz
2011 2011 Joint Urban Remote Sensing Event  
Unfortunately each imaging sensor has its own limits on scene recognition in the sense of thematic, temporal, and other interpretation.  ...  Datasets with different nature are integrated using the INFOFUSE framework [1], consisting of feature extraction (information fission), dimensionality reduction, and supervised classification.  ...  ACKNOWLEDGMENT We would like to thank European Space Imaging (EUSI) for provision of Digitalglobe WorldView-2 data. This work was supported by the DLR-DAAD research grant (A/09/95629).  ... 
doi:10.1109/jurse.2011.5764709 dblp:conf/jurse/MakarauPR11 fatcat:za5y7dorpjfgdnnraaumpnzlyq

Hyperspectral Remote Sensing for Detecting Soil Salinization Using ProSpecTIR-VS Aerial Imagery and Sensor Simulation

Odílio Rocha Neto, Adunias Teixeira, Raimundo Leão, Luis Moreira, Lênio Galvão
2017 Remote Sensing  
To investigate the influence of bandwidth and band positioning on the EC estimates, we simulated the spectral resolution of two hyperspectral sensors (airborne ProSpecTIR-VS and orbital Hyperspectral Infrared  ...  Imager (HyspIRI)) and three multispectral instruments (RapidEye/REIS, High Resolution Geometric (HRG)/SPOT-5, and Operational Land Imager (OLI)/Landsat-8)).  ...  (IFCE) for fieldwork assistance.  ... 
doi:10.3390/rs9010042 fatcat:ywq4lva6urc67mmxhc72wmrhkm

Next-Generation Optical Sensing Technologies for Exploring Ocean Worlds—NASA FluidCam, MiDAR, and NeMO-Net

Ved Chirayath, Alan Li
2019 Frontiers in Marine Science  
Chirayath and Li NASA FluidCam, MiDAR, and NeMO-Net using active learning for citizen-science based training. Preliminary results from four-class coral classification have an accuracy of 94.4%.  ...  VC and AL performed analysis for MiDAR and NeMO-Net results.  ...  Feature representation learned by the CNN on this dataset is then used to augment the feature representation of other regions.  ... 
doi:10.3389/fmars.2019.00521 fatcat:i2tlnhpimnbotkmwqnfa7kvidu

Radial Basis Function and Multilayer Perceptron neural networks for sea water optically active parameter estimation in case II waters: A comparison

G. Corsini, M. Diani, R. Grasso, M. De Martino, P. Mantero, S. Serpico
2003 International Journal of Remote Sensing  
For proving the concept we analyse the procedures and the performances on a simulated data set reproducing the data acquired from the MERIS (Medium Resolution Imaging Spectrometer), the multispectral sensor  ...  We analyse the neural networks approach applied to the estimation of chlorophyll concentration in coastal waters (Case II Waters) and discuss the use of two types of networks: the Radial Basis Function  ...  Acknowledgments This work was partially supported by the Italian Space Agency (ASI) within the framework of the project 'Experimental investigation of integrated methodologies for water colour analysis  ... 
doi:10.1080/0143116031000103781 fatcat:h7jn6hmwnfefrhe5hu36ei2k3e

Predicting Equivalent Water Thickness in Wheat Using UAV Mounted Multispectral Sensor through Deep Learning Techniques

Adama Traore, Syed Tahir Ata-Ul-Karim, Aiwang Duan, Mukesh Kumar Soothar, Seydou Traore, Ben Zhao
2021 Remote Sensing  
were used for estimating EWT.  ...  The selected VIs were used to estimate EWT using multiple linear regression (MLR), deep neural network multilayer perceptron (DNN-MLP), artificial neural networks multilayer perceptron (ANN-MLP), boosted  ...  The following steps were used for UAV image processing after UAV data collection. The pix4D mapper was used to process all the images into one large image using calibration images.  ... 
doi:10.3390/rs13214476 fatcat:3enqxieknfgirfzpgfwhroio5e

Machine Learning in Agriculture: A Comprehensive Updated Review

Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, Remigio Berruto, Dimitrios Kateris, Dionysis Bochtis
2021 Sensors  
The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop  ...  A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient.  ...  Water Management The agricultural sector constitutes the main consumer of available fresh water on a global scale, as plant growth largely relies on water availability.  ... 
doi:10.3390/s21113758 pmid:34071553 fatcat:moehdvs6efdpxpklidutmw2ary
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