Skip to main content

page search

Library Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery

Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery

Multi-scale segmentation approach for object-based land-cover classification using high-resolution imagery

Resource information

Date of publication
December 2014
Resource Language
ISBN / Resource ID
AGRIS:US201600057695
Pages
73-82

Image segmentation is a basic and important procedure in object-based classification of remote-sensing data. This study presents an approach to multi-scale optimal segmentation (OS), given that single-scale segmentation may not be the most suitable approach to map a variety of land-cover types characterized by various spatial structures; it objectively measures the appropriate segmentation scale for each object at various scales and projects them onto a single layer. A 1.8 m spatial resolution Worldview-2 image was used to perform successive multi-scale segmentations. The pixel standard deviation of an object was used to measure the optimal scale that occurred on the longest, feature unchanged scale range during multi-scale segmentation. Results indicate that the classification of multi-scale object OS can improve the overall accuracy by five percentage points compared to traditional single segmentation.

Share on RLBI navigator
NO

Authors and Publishers

Author(s), editor(s), contributor(s)

Zhang, Lei
Jia, Kun
Li, Xiaosong
Yuan, Quanzhi
Zhao, Xinfeng

Publisher(s)
Data Provider