Jungho Im
Cited by
Cited by
Support vector machines in remote sensing: A review
G Mountrakis, J Im, C Ogole
ISPRS journal of photogrammetry and remote sensing 66 (3), 247-259, 2011
Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data
J Rhee, J Im, GJ Carbone
Remote Sensing of environment 114 (12), 2875-2887, 2010
Object‐based change detection using correlation image analysis and image segmentation
J Im, JR Jensen, JA Tullis
International journal of remote sensing 29 (2), 399-423, 2008
Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification
Y Ke, LJ Quackenbush, J Im
Remote Sensing of Environment 114 (6), 1141-1154, 2010
A change detection model based on neighborhood correlation image analysis and decision tree classification
J Im, JR Jensen
Remote Sensing of Environment 99 (3), 326-340, 2005
Forest biomass estimation from airborne LiDAR data using machine learning approaches
CJ Gleason, J Im
Remote Sensing of Environment 125, 80-91, 2012
Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations
Y Ke, J Im, J Lee, H Gong, Y Ryu
Remote sensing of environment 164, 298-313, 2015
Drought assessment and monitoring through blending of multi-sensor indices using machine learning approaches for different climate regions
S Park, J Im, E Jang, J Rhee
Agricultural and forest meteorology 216, 157-169, 2016
Meteorological drought forecasting for ungauged areas based on machine learning: Using long-range climate forecast and remote sensing data
J Rhee, J Im
Agricultural and Forest Meteorology 237, 105-122, 2017
Machine learning approaches to coastal water quality monitoring using GOCI satellite data
YH Kim, J Im, HK Ha, JK Choi, S Ha
GIScience & Remote Sensing 51 (2), 158-174, 2014
Downscaling of AMSR-E soil moisture with MODIS products using machine learning approaches
J Im, S Park, J Rhee, J Baik, M Choi
Environmental Earth Sciences 75, 1-19, 2016
Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images
C Yoo, D Han, J Im, B Bechtel
ISPRS Journal of Photogrammetry and Remote Sensing 157, 155-170, 2019
Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest
M Li, J Im, C Beier
GIScience & Remote Sensing 50 (4), 361-384, 2013
Downscaling of MODIS One kilometer evapotranspiration using Landsat-8 data and machine learning approaches
Y Ke, J Im, S Park, H Gong
Remote Sensing 8 (3), 215, 2016
Evaluating five remote sensing based single-source surface energy balance models for estimating daily evapotranspiration in a humid subtropical climate
N Bhattarai, SB Shaw, LJ Quackenbush, J Im, R Niraula
International Journal of Applied Earth Observation and Geoinformation 49, 75-86, 2016
Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula
S Park, J Im, S Park, J Rhee
Agricultural and Forest Meteorology 237, 257-269, 2017
Classification and mapping of paddy rice by combining Landsat and SAR time series data
S Park, J Im, S Park, C Yoo, H Han, J Rhee
Remote Sensing 10 (3), 447, 2018
Estimation of daily maximum and minimum air temperatures in urban landscapes using MODIS time series satellite data
C Yoo, J Im, S Park, LJ Quackenbush
ISPRS journal of photogrammetry and remote sensing 137, 149-162, 2018
A review of remote sensing of forest biomass and biofuel: options for small-area applications
CJ Gleason, J Im
GIScience & Remote Sensing 48 (2), 141-170, 2011
Object-based land cover classification using high-posting-density LiDAR data
J Im, JR Jensen, ME Hodgson
GIScience & Remote Sensing 45 (2), 209-228, 2008
The system can't perform the operation now. Try again later.
Articles 1–20