Multi-view object-based classification of wetland land covers using unmanned aircraft system images
暂无分享,去创建一个
[1] F. R. Schiebe,et al. Canopy attributes of desert grassland and transition communities derived from multiangular airborne imagery , 2003 .
[2] Thomas Blaschke,et al. Object based image analysis for remote sensing , 2010 .
[3] Tao Liu,et al. Object-based classification of wetland vegetation using very high-resolution unmanned air system imagery , 2017 .
[4] William J. Emery,et al. Very High Resolution Multiangle Urban Classification Analysis , 2012, IEEE Transactions on Geoscience and Remote Sensing.
[5] J. Ross. The radiation regime and architecture of plant stands , 1981, Tasks for vegetation sciences 3.
[6] Françoise Viénot,et al. Wheat leaf bidirectional reflectance measurements: Description and quantification of the volume, specular and hot-spot scattering features , 2012 .
[7] Jungho Im,et al. Forest Biomass and Carbon Stock Quantification Using Airborne LiDAR Data: A Case Study Over Huntington Wildlife Forest in the Adirondack Park , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[8] Geoffrey A. Hollinger,et al. Aerial Vehicle Path Planning for Monitoring Wildfire Frontiers , 2015, FSR.
[9] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[10] A. Rango,et al. Image Processing and Classification Procedures for Analysis of Sub-decimeter Imagery Acquired with an Unmanned Aircraft over Arid Rangelands , 2011 .
[11] Neha Gupta,et al. Object based Information Extraction from High Resolution Satellite Imagery using eCognition , 2014 .
[12] A. Strahler,et al. Classification of ASAS multiangle and multispectral measurements using artificial neural networks , 1996 .
[13] Jungho Im,et al. Forest biomass estimation from airborne LiDAR data using machine learning approaches , 2012 .
[14] Lindi J. Quackenbush,et al. Optimum Scale in Object‐Based Image Analysis , 2014 .
[15] F. López-Granados,et al. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV , 2014 .
[16] Faith R. Kearns,et al. Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography , 2008, Comput. Environ. Urban Syst..
[17] P. Wolf,et al. Elements of Photogrammetry(with Applications in GIS) , 2000 .
[18] Tae Ho Lee,et al. A Study on Land Cover Map of UAV Imagery using an Object-based Classification Method , 2015 .
[19] S. Sandmeier,et al. Structure Analysis and Classification of Boreal Forests Using Airborne Hyperspectral Brdf Data from Asas Imagery and Processing Techniques Have Also Been Used Potential for Combining Both High Spectral Resolution And , 2022 .
[20] Elizabeth H. Boughton,et al. Trade-Offs Among Ecosystem Services and Disservices on a Florida Ranch , 2013 .
[21] Dongmei Chen,et al. Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .
[22] Bing Zhang,et al. Measurement and Analysis of Bidirectional SIF Emissions in Wheat Canopies , 2016, IEEE Transactions on Geoscience and Remote Sensing.
[23] Taro Suzuki,et al. Generation of Large Mosaic Images for Vegetation Monitoring Using a Small Unmanned Aerial Vehicle , 2010, J. Robotics Mechatronics.
[24] Changchun Li,et al. The Application of Unmanned Aerial Vehicle Remote Sensing in Quickly Monitoring Crop Pests , 2012, Intell. Autom. Soft Comput..
[25] J. Townshend,et al. Bidirectional effects in Landsat reflectance estimates: Is there a problem to solve? , 2015 .
[26] Fabio Del Frate,et al. Hyperspectral and Multiangle CHRIS–PROBA Images for the Generation of Land Cover Maps , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[27] Alessandro Matese,et al. A flexible unmanned aerial vehicle for precision agriculture , 2012, Precision Agriculture.
[28] Patrick R. Harrison,et al. Learning class descriptions from a data base of spectral reflectance with multiple view angles , 1992, IEEE Trans. Geosci. Remote. Sens..
[29] Michael D. King,et al. Airborne spectral BRDF of various surface types (ocean, vegetation, snow, desert, wetlands, cloud decks, smoke layers) for remote sensing applications , 2016 .
[30] Korehisa Kaneko,et al. Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method , 2014 .
[31] Jungho Im,et al. A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification , 2015 .
[32] O. Csillik,et al. Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[33] Andrew W. Fitzgibbon,et al. Bundle Adjustment - A Modern Synthesis , 1999, Workshop on Vision Algorithms.
[34] Giles M. Foody,et al. Using mixed objects in the training of object-based image classifications , 2017 .
[35] Dharma P. Agrawal,et al. Communication and networking of UAV-based systems: Classification and associated architectures , 2017, J. Netw. Comput. Appl..
[36] Bai Zhang,et al. Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data , 2017 .
[37] S. Luke Flory,et al. Cogongrass (Imperata cylindrica) invasions in the US: Mechanisms, impacts, and threats to biodiversity , 2015 .
[38] Tim M. O'Brien. Small Unmanned Aerial Vehicles as Remote Sensors: An Effective Data Gathering Tool for Wetland Mapping , 2016 .
[39] C. Atzberger,et al. Evaluation of semi-empirical BRDF models inverted against multi-angle data from a digital airborne frame camera for enhancing forest type classification , 2014 .
[40] Tao Liu,et al. Evaluating the potential of multi-view data extraction from small Unmanned Aerial Systems (UASs) for object-based classification for Wetland land covers , 2018, GIScience & Remote Sensing.
[41] Paolo Gamba,et al. Improvements to urban area characterization using multitemporal and multiangle SAR images , 2003, IEEE Trans. Geosci. Remote. Sens..
[42] Koichi Amaha,et al. Canopy height measurement by photogrammetric analysis of aerial images: Application to buckwheat (Fagopyrum esculentum Moench) lodging evaluation , 2012 .
[43] Amr H. Abd-Elrahman,et al. An Object-Based Image Analysis Method for Enhancing Classification of Land Covers Using Fully Convolutional Networks and Multi-View Images of Small Unmanned Aerial System , 2018, Remote. Sens..
[44] E. Milton,et al. The use of the empirical line method to calibrate remotely sensed data to reflectance , 1999 .
[45] Li Minzan,et al. Precision management of winter wheat based on aerial images and hyperspectral data obtained by unmanned aircraft , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..
[46] F. J. García-Haro,et al. Airborne measurement of hot spot reflectance signatures , 2004 .
[47] H. Rahman,et al. Coupled surface-atmosphere reflectance (CSAR) model: 2. Semiempirical surface model usable with NOAA advanced very high resolution radiometer data , 1993 .
[48] David J. Selkowitz,et al. A comparison of multi-spectral, multi-angular, and multi-temporal remote sensing datasets for fractional shrub canopy mapping in Arctic Alaska , 2010 .
[49] Juan López,et al. Architecture for a helicopter-based unmanned aerial systems wildfire surveillance system , 2011 .
[50] F. M. Roka,et al. Integrating Ranch Forage Production, Cattle Performance, and Economics in Ranch Management Systems for Southern Florida , 2007 .
[51] H. Franklin Percival,et al. Use of Unmanned Aircraft Systems to Delineate Fine-Scale Wetland Vegetation Communities , 2014, Wetlands.
[52] Thomas Blaschke,et al. Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.
[53] J. Roujean,et al. A bidirectional reflectance model of the Earth's surface for the correction of remote sensing data , 1992 .
[54] Eric C. Brown de Colstoun,et al. Improving global scale land cover classifications with multi-directional POLDER data and a decision tree classifier , 2006 .
[55] Michael S. Ross,et al. Vegetation Classification for South Florida Natural Areas , 2006 .
[56] Anne W. Nolin,et al. Classification of glacier zones in western Greenland using albedo and surface roughness from the Multi-angle Imaging SpectroRadiometer (MISR) , 2007 .
[57] Michael Ol,et al. Small UAV Research and Evolution in Long Endurance Electric Powered Vehicles , 2007 .
[58] Debra P. C. Peters,et al. Support vector machines for recognition of semi-arid vegetation types using MISR multi-angle imagery , 2007 .
[59] Alan H. Strahler,et al. Improving MODIS land cover classification by combining MODIS spectral and angular signatures in a Canadian boreal forest , 2011 .
[60] Jianya Gong,et al. Angular difference feature extraction for urban scene classification using ZY-3 multi-angle high-resolution satellite imagery , 2018 .
[61] T. Schmugge,et al. Research Article: Using Unmanned Aerial Vehicles for Rangelands: Current Applications and Future Potentials , 2006 .
[62] Yuhong He,et al. Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland , 2017 .
[63] Tao Liu,et al. Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification , 2018 .
[64] Tao Liu,et al. Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system , 2018 .
[65] V. N. Sridhar,et al. Impact of Surface Anisotropy on Classification Accuracy of Selected Vegetation Classes: An Evaluation Using Multidate Multiangular MISR Data Over Parts of Madhya Pradesh, India , 2008, IEEE Transactions on Geoscience and Remote Sensing.
[66] K. McGwire,et al. Multiscale assessment of green leaf cover in a semi-arid rangeland with a small unmanned aerial vehicle , 2013 .
[67] L. G. Holm,et al. The world's worst weeds. , 1977 .
[68] Lindi J. Quackenbush,et al. Impact of training and validation sample selection on classification accuracy and accuracy assessment when using reference polygons in object-based classification , 2013 .
[69] Philip Moore,et al. ACCURACY ASSESSMENT OF A UAV-BASED LANDSLIDE MONITORING SYSTEM , 2016 .
[70] Jorge Delgado,et al. Analysis of Landslide Evolution Affecting Olive Groves Using UAV and Photogrammetric Techniques , 2016, Remote. Sens..
[71] V. Salokhe,et al. Application of low altitude remote sensing (LARS) platform for monitoring crop growth and weed infestation in a soybean plantation , 2012, Precision Agriculture.
[72] Clement Atzberger,et al. Potential of Multi-Angular Data Derived From a Digital Aerial Frame Camera for Forest Classification , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.
[73] P. Gong,et al. Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .