UAV & satellite synergies for optical remote sensing applications: A literature review

[1]  R. Antoine,et al.  Geoscientists in the Sky: Unmanned Aerial Vehicles Responding to Geohazards , 2020, Surveys in Geophysics.

[2]  Patrice E. Carbonneau,et al.  UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes , 2020, Earth Surface Processes and Landforms.

[3]  Thomas Houet,et al.  Can UAVs fill the gap between in situ surveys and satellites for habitat mapping? , 2020, Remote Sensing of Environment.

[4]  P. Carbonneau,et al.  UAV‐based training for fully fuzzy classification of Sentinel‐2 fluvial scenes , 2020, Earth Surface Processes and Landforms.

[5]  Uwe Rascher,et al.  Multi-Scale Evaluation of Drone-Based Multispectral Surface Reflectance and Vegetation Indices in Operational Conditions , 2020, Remote. Sens..

[6]  Wei Liu,et al.  Accurate Building Extraction from Fused DSM and UAV Images Using a Chain Fully Convolutional Neural Network , 2019, Remote. Sens..

[7]  Zhenyu Tan,et al.  An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion , 2019, Remote. Sens..

[8]  Gregory Giuliani,et al.  Earth Observation Open Science: Enhancing Reproducible Science Using Data Cubes , 2019, Data.

[9]  Dionysios Apostolopoulos,et al.  Combination of Aerial, Satellite, and UAV Photogrammetry for Mapping the Diachronic Coastline Evolution: The Case of Lefkada Island , 2019, ISPRS Int. J. Geo Inf..

[10]  Jakob J. Assmann,et al.  Illumination Geometry and Flying Height Influence Surface Reflectance and NDVI Derived from Multispectral UAS Imagery , 2019, Drones.

[11]  Alexander Y. Sun,et al.  How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions , 2019, Environmental Research Letters.

[12]  Michael Förster,et al.  UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data , 2019, Remote Sensing of Environment.

[13]  J. Martínez-Sánchez,et al.  UAV AND SATELLITE IMAGERY APPLIED TO ALIEN SPECIES MAPPING IN NW SPAIN , 2019, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[14]  Arko Lucieer,et al.  Ultra-high spatial resolution fractional vegetation cover from unmanned aerial multispectral imagery , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[15]  S. Hese,et al.  INTRASEASONAL VARIABILITY OF GUANO STAINS IN A REMOTELY SENSED PENGUIN COLONY USING UAV AND SATELLITE , 2019, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[16]  Alfred Stein,et al.  Spatiotemporal Image Fusion in Remote Sensing , 2019, Remote. Sens..

[17]  Miska Luoto,et al.  Estimating fractional cover of tundra vegetation at multiple scales using unmanned aerial systems and optical satellite data , 2019, Remote Sensing of Environment.

[18]  Suming Zhang,et al.  Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the SPAD of Winter Wheat in the Reviving Stage , 2019, Sensors.

[19]  Seth M. Munson,et al.  Invasive buffelgrass detection using high‐resolution satellite and UAV imagery on Google Earth Engine , 2019, Remote Sensing in Ecology and Conservation.

[20]  Marcello Chiaberge,et al.  Comparison of Satellite and UAV-Based Multispectral Imagery for Vineyard Variability Assessment , 2019, Remote. Sens..

[21]  Benoît St-Onge,et al.  Estimating the Height and Basal Area at Individual Tree and Plot Levels in Canadian Subarctic Lichen Woodlands Using Stereo WorldView-3 Images , 2019, Remote. Sens..

[22]  M. Goraj,et al.  Free water table area monitoring on wetlands using satellite and UAV orthophotomaps – Kampinos National Park case study , 2019, Meteorology Hydrology and Water Management.

[23]  Konstantinos N. Topouzelis,et al.  Coastal Management Using UAS and High-Resolution Satellite Images for Touristic Areas , 2019, Int. J. Appl. Geospat. Res..

[24]  Jon Atli Benediktsson,et al.  Multisource and Multitemporal Data Fusion in Remote Sensing , 2018, ArXiv.

[25]  Anita Simic Milas,et al.  Classification of shoreline vegetation in the Western Basin of Lake Erie using airborne hyperspectral imager HSI2, Pleiades and UAV data , 2018, International Journal of Remote Sensing.

[26]  Timothy A. Warner,et al.  Drones – the third generation source of remote sensing data , 2018, International Journal of Remote Sensing.

[27]  M. Trnka,et al.  Estimating Crop Yields at the Field Level Using Landsat and MODIS Products , 2018, Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis.

[28]  Xavier Pons,et al.  Radiometric Correction of Landsat-8 and Sentinel-2A Scenes Using Drone Imagery in Synergy with Field Spectroradiometry , 2018, Remote. Sens..

[29]  J. Hornbuckle,et al.  Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery , 2018, Water.

[30]  T. Sankey,et al.  Mapping and measuring aeolian sand dunes with photogrammetry and LiDAR from unmanned aerial vehicles (UAV) and multispectral satellite imagery on the Paria Plateau, AZ, USA , 2018, Geomorphology.

[31]  Jianyu Chen,et al.  Automatic extraction of yardangs using Landsat 8 and UAV images: A case study in the Qaidam Basin, China , 2018, Aeolian Research.

[32]  N. Koedam,et al.  The advantages of using drones over space-borne imagery in the mapping of mangrove forests , 2018, PloS one.

[33]  Michael Förster,et al.  Application of a One-Class Classifier and a Linear Spectral Unmixing Method for Detecting Invasive Species in Central Chile , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[34]  Dimitar Misev,et al.  Datacubes: Towards Space/Time Analysis-Ready Data , 2018, Lecture Notes in Geoinformation and Cartography.

[35]  Luxon Nhamo,et al.  Improving the Accuracy of Remotely Sensed Irrigated Areas Using Post-Classification Enhancement Through UAV Capability , 2018, Remote. Sens..

[36]  Qian Du,et al.  Remote Sensing Big Data: Theory, Methods and Applications , 2018, Remote. Sens..

[37]  R. Houborg,et al.  A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data , 2018 .

[38]  Meshal M. Abdullah,et al.  Satellite vs. UAVs Remote Sensing of Arid Ecosystems: A Review with in an Ecological Perspective , 2018 .

[39]  Pierre Soille,et al.  A versatile data-intensive computing platform for information retrieval from big geospatial data , 2018, Future Gener. Comput. Syst..

[40]  Xiaolin Zhu,et al.  Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions , 2018, Remote. Sens..

[41]  Zhenfeng Shao,et al.  Remote Sensing Image Fusion With Deep Convolutional Neural Network , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[42]  Sammy A. Perdomo,et al.  RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields , 2018, Precision Agriculture.

[43]  Pierre Defourny,et al.  Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context , 2018, Remote. Sens..

[44]  Hui Liang,et al.  Fractional Snow-Cover Mapping Based on MODIS and UAV Data over the Tibetan Plateau , 2017, Remote. Sens..

[45]  Han Liu,et al.  Estimating Rangeland Forage Production Using Remote Sensing Data from a Small Unmanned Aerial System (sUAS) and PlanetScope Satellite , 2017, Remote. Sens..

[46]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[47]  Agnieszka Jenerowicz,et al.  The fusion of satellite and UAV data: simulation of high spatial resolution band , 2017, Remote Sensing.

[48]  Du Mengmeng,et al.  Multi-temporal monitoring of wheat growth by using images from satellite and unmanned aerial vehicle , 2017 .

[49]  Houshang Darabi,et al.  LSTM Fully Convolutional Networks for Time Series Classification , 2017, IEEE Access.

[50]  Mac McKee,et al.  Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture , 2017, Sensors.

[51]  P. Pyšek,et al.  Timing Is Important: Unmanned Aircraft vs. Satellite Imagery in Plant Invasion Monitoring , 2017, Front. Plant Sci..

[52]  Mohammad Kakooei,et al.  Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment , 2017 .

[53]  Scot E. Smith,et al.  Mapping orangutan habitat and agricultural areas using Landsat OLI imagery augmented with unmanned aircraft system aerial photography , 2017 .

[54]  Andrew Marx,et al.  UAV data for multi-temporal Landsat analysis of historic reforestation: a case study in Costa Rica , 2017 .

[55]  Arthur P. Cracknell,et al.  UAVs: regulations and law enforcement , 2017 .

[56]  Jan G. P. W. Clevers,et al.  Mapping Reflectance Anisotropy of a Potato Canopy Using Aerial Images Acquired with an Unmanned Aerial Vehicle , 2017, Remote. Sens..

[57]  Ben Evans,et al.  The Australian Geoscience Data Cube - foundations and lessons learned , 2017 .

[58]  K. Meissner,et al.  How Essential Biodiversity Variables and remote sensing can help national biodiversity monitoring , 2017 .

[59]  Veronica Tofani,et al.  Spaceborne, UAV and ground-based remote sensing techniques for landslide mapping, monitoring and early warning , 2017, Geoenvironmental Disasters.

[60]  Ainong Li,et al.  Subpixel Inundation Mapping Using Landsat-8 OLI and UAV Data for a Wetland Region on the Zoige Plateau, China , 2017, Remote. Sens..

[61]  Leonidas J. Guibas,et al.  PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  Qing Wang,et al.  Gully Erosion Mapping and Monitoring at Multiple Scales Based on Multi-Source Remote Sensing Data of the Sancha River Catchment, Northeast China , 2016, ISPRS Int. J. Geo Inf..

[63]  Jie Li,et al.  Image super-resolution: The techniques, applications, and future , 2016, Signal Process..

[64]  Charalambos Kontoes,et al.  A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images , 2016 .

[65]  Aurélien Plyer,et al.  Adaptation and Evaluation of an Optical Flow Method Applied to Coregistration of Forest Remote Sensing Images , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[66]  Ainong Li,et al.  Grassland fractional vegetation cover monitoring using the composited HJ-1A/B time series images and unmanned aerial vehicles: A case study in Zoige wetland, China , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[67]  Venkatesh Saligrama,et al.  A multi-resolution approach for discovery and 3-D modeling of archaeological sites using satellite imagery and a UAV-borne camera , 2016, 2016 American Control Conference (ACC).

[68]  Petr Dvorak,et al.  DOES THE DATA RESOLUTION/ORIGIN MATTER? SATELLITE, AIRBORNE AND UAV IMAGERY TO TACKLE PLANT INVASIONS , 2016 .

[69]  E. Matoušková,et al.  USING REMOTELY SENSED DATA FOR DOCUMENTATION OF ARCHAEOLOGICAL SITES IN NORTHEASTERN MESOPOTAMIA , 2016 .

[70]  Antonio Conceição Paranhos Filho,et al.  Water Quality and Chlorophyll Measurement Through Vegetation Indices Generated from Orbital and Suborbital Images , 2016, Water, Air, & Soil Pollution.

[71]  YangQuan Chen,et al.  An analysis of the effect of the bidirectional reflectance distribution function on remote sensing imagery accuracy from Small Unmanned Aircraft Systems , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[72]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.

[73]  Christopher Spence,et al.  Deployment of an unmanned aerial system to assist in mapping an intermittent stream , 2016 .

[74]  Claudio Smiraglia,et al.  High-resolution mapping of glacier surface features. The UAV survey of the Forni Glacier (Stelvio National Park, Italy) , 2015 .

[75]  John A. Gamon,et al.  Reviews and Syntheses: optical sampling of the flux tower footprint , 2015 .

[76]  Stefan Dech,et al.  Remote Sensing Time Series: Revealing Land Surface Dynamics , 2015 .

[77]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[78]  Piero Toscano,et al.  Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture , 2015, Remote. Sens..

[79]  Juha Suomalainen,et al.  Generation of Spectral–Temporal Response Surfaces by Combining Multispectral Satellite and Hyperspectral UAV Imagery for Precision Agriculture Applications , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[80]  Trevor Darrell,et al.  Fully convolutional networks for semantic segmentation , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[81]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[82]  Juha Suomalainen,et al.  Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications , 2014, 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[83]  Jiann-Yeou Rau,et al.  Semiautomatic Object-Oriented Landslide Recognition Scheme From Multisensor Optical Imagery and DEM , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[84]  Peter M. Atkinson,et al.  Downscaling in remote sensing , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[85]  Henri Eisenbeiss,et al.  UAV PHOTOGRAMMETRY IN REMOTE AREAS – 3D MODELING OF DRAPHAM DZONG BHUTAN , 2012 .

[86]  Irene Marzolff,et al.  Monitoring Soil Erosion in the Souss Basin, Morocco, with a multiscale Object-based Remote Sensing Approach using UAV and Satellite Data , 2011 .

[87]  Charles K. Toth,et al.  Automatic Georeferencing of Aerial Images Using Stereo High-Resolution Satellite Images , 2011 .

[88]  Kostas Stamatiou,et al.  Combining GeoEye-1 Satellite Remote Sensing, UAV Aerial Imaging, and Geophysical Surveys in Anomaly Detection Applied to Archaeology , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[89]  Steven Hosford,et al.  HYPXIM: A new hyperspectral sensor combining science/defence applications , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[90]  H. R. Matinfar,et al.  Criteria of selecting satellite data for studying land resources , 2010 .

[91]  Jixian Zhang Multi-source remote sensing data fusion: status and trends , 2010 .

[92]  J. Maslanik,et al.  Derivation of melt pond coverage on Arctic sea ice using MODIS observations , 2008 .

[93]  Debra P. C. Peters,et al.  Differentiation of semi‐arid vegetation types based on multi‐angular observations from MISR and MODIS , 2007 .

[94]  Mathew R. Schwaller,et al.  On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[95]  T. Painter,et al.  Reflectance quantities in optical remote sensing - definitions and case studies , 2006 .

[96]  John V. Martonchik,et al.  Retrieval of Surface Directional Reflectance Properties Using Ground Level Multiangle Measurements , 1994 .

[97]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[98]  Stefan Dech,et al.  Remote Sensing Time Series Revealing Land Surface Dynamics: Status Quo and the Pathway Ahead , 2015 .

[99]  K. Jacobsen Airborne or Spaceborne Images for Topographic Mapping , 2012 .

[100]  Christine Pohl,et al.  Multisensor image fusion in remote sensing: concepts, methods and applications , 1998 .