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

Abstract

AbstractImage 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. FundingThis research was funded by the project of National Natural Science Foundation of China [41371361]; the 'Strategic Priority Research Program – Climate Change: Carbon Budget and Related Issues' of the Chinese Academy of Sciences [XDA05050109]; the Inventory and Assessment of National Ecological Environment 10 year Changes (2000–2010) using remote sensing.