A Multilevel Hierarchical Image Segmentation Method for Urban Impervious Surface Mapping Using Very High Resolution Imagery

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2011-03, Vol.4 (1), p.103-116
• Main Authors: Li, Peijun, Guo, Jiancong, Song, Benqin, Xiao, Xiaobai
• Format: Article
• Language: English
• Subjects: Accuracy; Classification; Contours; Dynamics; Hierarchical segmentation; high resolution imagery; Histograms; Image segmentation; impervious surface; Mapping; object based classification; Pixel; Remote sensing; Segmentation; Studies; Surface morphology; Transformations; Urban areas; Watersheds
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2010.2074186

This paper presents a hierarchical image segmentation method that combines multichannel watershed transformation and dynamics of watershed contours for the segmentation of very high resolution (VHR) multispectral imagery. The image gradient was first extracted from a multispectral image using a multichannel morphological method, followed by classical watershed transformation to produce an initial segmentation result. The resulting watershed contours were then analyzed according to their relevance relative to the minima of the adjacent basins to construct an image containing information about their dynamics. By thresholding the image of the contour dynamics at different levels, multilevel hierarchical segmentation results with different levels of detail were achieved. The proposed method was evaluated by comparing with existing methods through visual inspection, quantitative measures and applications in urban impervious surface mapping, using two sets of VHR image data. The experimental results showed that the proposed method produced more accurate segmentation results compared to an existing single-level segmentation method, in terms of visual and quantitative evaluations. While used for urban impervious surface mapping, the proposed method achieved an overall accuracy significantly higher than the pixel based classification method, and also higher than the object based classification using a single-level segmentation result. Compared with the most widely used segmentation method implemented in the eCognition, the proposed method achieved a comparable performance, although they have different segmentation details. The proposed segmentation method can be used in relevant VHR image processing and applications.