Remote Sensing | |
Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points | |
Leena Matikainen1  | |
关键词: land cover; segmentation; classification; benchmarking; aerial image; satellite image; laser scanning; SAR; classification tree; object-based; | |
DOI : 10.3390/rs3081777 | |
来源: mdpi | |
【 摘 要 】
In order to better understand and exploit the rich information content of new remotely sensed datasets, there is a need for comparative land cover classification studies. In this study, the automatic classification of a suburban area was investigated by using (1) digital aerial image data; (2) digital aerial image data and laser scanner data; (3) a high-resolution optical QuickBird satellite image; (4) high-resolution airborne synthetic aperture radar (SAR) data; and (5) SAR data and laser scanner data. A segment-based approach was applied. The classification rules for distinguishing
【 授权许可】
CC BY
© 2011 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190048545ZK.pdf | 2405KB | download |