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

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 1.8m 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.