Abstract
Abstract. In this paper a new object-based framework is developed for automate scale selection in image segmentation. The quality of image objects have an important impact on further analyses. Due to the strong dependency of segmentation results to the scale parameter, choosing the best value for this parameter, for each class, becomes a main challenge in object-based image analysis. We propose a new framework which employs pixel-based land cover map to estimate the initial scale dedicated to each class. These scales are used to build segmentation scale space (SSS), a hierarchy of image objects. Optimization of SSS, respect to NDVI and DSM values in each super object is used to get the best scale in local regions of image scene. Optimized SSS segmentations are finally classified to produce the final land cover map. Very high resolution aerial image and digital surface model provided by ISPRS 2D semantic labelling dataset is used in our experiments. The result of our proposed method is comparable to those of ESP tool, a well-known method to estimate the scale of segmentation, and marginally improved the overall accuracy of classification from 79% to 80%.
Highlights
Land cover information about the earth’s surface is critical in most earth and environmental engineering applications (Berger et al, 2013)
F1-score car Object creation through the segmentation algorithm is a main processing step in object-based image analysis process. It highly depends on the segmentation scale parameter
A new framework is proposed for estimating segmentation scale parameter
Summary
Land cover information about the earth’s surface is critical in most earth and environmental engineering applications (Berger et al, 2013). Aerial and satellite remote sensing images provide fast, cheap and accurate data source in land cover mapping. Machine learning methods are employed to produce land cover maps using remote sensing images. Different parametric (e.g. maximum likelihood) and non-parametric (e.g. K-nearest neighbours and support vector machine) methods are developed to improve the accuracy of predicting the proper label for unknown pixels in the image. A comprehensive review on classification methods in remote sensing land cover mapping could be found in (Lu and Weng, 2007)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
