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Lei Zhang^a, Kun Jia^b*, Xiaosong Li^a, Quanzhi Yuan^a & Xinfeng Zhao^a

 

^a Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China;

^b College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.

 

ABSTRACT

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.

 

PUBLISHED BY: REMOTE SENSING LETTERS, 2014, 5 (1): 73-82.

 

SOURCE: http://www.tandfonline.com/doi/pdf/10.1080/2150704X.2013.875235