J
Jungho Im
Researcher at Ulsan National Institute of Science and Technology
Publications - 161
Citations - 9250
Jungho Im is an academic researcher from Ulsan National Institute of Science and Technology. The author has contributed to research in topics: Environmental science & Computer science. The author has an hindex of 40, co-authored 134 publications receiving 6982 citations. Previous affiliations of Jungho Im include University of South Carolina & State University of New York System.
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Support vector machines in remote sensing: A review
TL;DR: This paper reviews remote sensing implementations of support vector machines (SVMs), a promising machine learning methodology that is particularly appealing in the remote sensing field due to their ability to generalize well even with limited training samples.
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Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data
TL;DR: In this article, the authors proposed a new remote sensing-based drought index, the Scaled Drought Condition Index (SDCI), for agricultural drought monitoring in both arid and humid regions using multi-sensor data.
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Object-based change detection using correlation image analysis and image segmentation
TL;DR: This study introduces change detection based on object/neighbourhood correlation image analysis and image segmentation techniques and found that object‐based change classifications incorporating the OCIs or the NCIs produced more accurate change detection classes than other change detection results.
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Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification
TL;DR: Quantitative segmentation quality assessment and classification accuracy results showed the integration of spectral and LIDAR data, in both image segmentation and object-based classification, improved the forest classification compared to using either data source independently.
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A change detection model based on neighborhood correlation image analysis and decision tree classification
Jungho Im,John R. Jensen +1 more
TL;DR: A change detection model based on Neighborhood Correlation Image logic, based on the fact that the same geographic area on two dates of imagery will tend to be highly correlated if little change has occurred, and uncorrelated when change occurs, is introduced.