Timothy A. Warner
Timothy A. Warner
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Cited by
Cited by
Implementation of machine-learning classification in remote sensing: An applied review
AE Maxwell, TA Warner, F Fang
International journal of remote sensing 39 (9), 2784-2817, 2018
Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America
T Brandtberg, TA Warner, RE Landenberger, JB McGraw
Remote sensing of Environment 85 (3), 290-303, 2003
A comparison of multispectral and multitemporal information in high spatial resolution imagery for classification of individual tree species in a temperate hardwood forest
T Key, TA Warner, JB McGraw, MA Fajvan
Remote Sensing of Environment 75 (1), 100-112, 2001
Forest type mapping using object-specific texture measures from multispectral Ikonos imagery
M Kim, M Madden, TA Warner
Photogrammetric Engineering & Remote Sensing 75 (7), 819-829, 2009
Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification
AE Ramezan, C.A., Warner, T.A., and Maxwell
Remote Sensing 11 (2), 185, 2019
Estimation of optimal image object size for the segmentation of forest stands with multispectral IKONOS imagery
M Kim, M Madden, T Warner
Object-based image analysis: spatial concepts for knowledge-driven remote …, 2008
Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects
M Kim, TA Warner, M Madden, DS Atkinson
International Journal of Remote Sensing 32 (10), 2825-2850, 2011
Kernel-based extreme learning machine for remote-sensing image classification
M Pal, AE Maxwell, TA Warner
Remote Sensing Letters 4 (9), 853-862, 2013
Apartheid representations in a digital landscape: GIS, remote sensing and local knowledge in Kiepersol, South Africa
D Weiner, TA Warner, TM Harris, RM Levin
Cartography and Geographic Information Systems 22 (1), 30-44, 1995
Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—Part 1: Literature review
AE Maxwell, TA Warner, LA Guillén
Remote Sensing 13 (13), 2450, 2021
Integrating visible, near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite, Nevada
X Chen, TA Warner, DJ Campagna
Remote Sensing of Environment 110 (3), 344-356, 2007
Multi-scale linkages between topographic attributes and vegetation indices in a mountainous landscape
Y Deng, X Chen, E Chuvieco, T Warner, JP Wilson
Remote Sensing of Environment 111 (1), 122-134, 2007
Change detection accuracy and image properties: a study using simulated data
A Almutairi, TA Warner
Remote Sensing 2 (6), 1508-1529, 2010
The SAGE Handbook of Remote Sensing
TA Warner, GM Foody, MD Nellis
The SAGE Handbook of Remote Sensing, 1-568, 2009
A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data
Y He, E Lee, TA Warner
Remote Sensing of Environment 199, 201-217, 2017
Using Thematic Mapper data for change detection and sustainable use of cultivated land: a case study in the Yellow River delta, China
GX Zhao, G Lin, T Warner
International Journal of Remote Sensing 25 (13), 2509-2522, 2004
Effects of training set size on supervised machine-learning land-cover classification of large-area high-resolution remotely sensed data
CA Ramezan, TA Warner, AE Maxwell, BS Price
Remote Sensing 13 (3), 368, 2021
Assessing machine-learning algorithms and image-and lidar-derived variables for GEOBIA classification of mining and mine reclamation
AE Maxwell, TA Warner, MP Strager, JF Conley, AL Sharp
International Journal of Remote Sensing 36 (4), 954-978, 2015
Delineation and identification of individual trees in the eastern deciduous forest
T Warner
Proceedings of the international forum on automated interpretation of high …, 1999
Large-area, high spatial resolution land cover mapping using random forests, GEOBIA, and NAIP orthophotography: Findings and recommendations
AE Maxwell, MP Strager, TA Warner, CA Ramezan, AN Morgan, ...
Remote Sensing 11 (12), 1409, 2019
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