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Timothy A. Warner
Timothy A. Warner
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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
18482018
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
6152003
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
3312001
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
3002019
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
2552021
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
2352009
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
2282008
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
2242011
Kernel-based extreme learning machine for remote-sensing image classification
M Pal, AE Maxwell, TA Warner
Remote Sensing Letters 4 (9), 853-862, 2013
2172013
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
1732021
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
1601995
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
1352007
Change detection accuracy and image properties: a study using simulated data
A Almutairi, TA Warner
Remote Sensing 2 (6), 1508-1529, 2010
1282010
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
1262007
The SAGE Handbook of Remote Sensing
TA Warner, GM Foody, MD Nellis
The SAGE Handbook of Remote Sensing, 1-568, 2009
1212009
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
1182017
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
1162004
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
982015
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
882019
Accuracy assessment in convolutional neural network-based deep learning remote sensing studies—Part 2: Recommendations and best practices
AE Maxwell, TA Warner, LA Guillén
Remote Sensing 13 (13), 2591, 2021
852021
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