A comparative study of the segmentation of weighted aggregation and multiresolution segmentation. Issue 5 (2nd September 2016)

• Record Type: Journal Article
• Authors: Du, Shihong; Guo, Zhou; Wang, Wanyi; Guo, Luo; Nie, Juan
• Is Part Of: GIScience & remote sensing. Volume 53:Issue 5(2016)
• Page Start: 651
• Page End: 670
• Publication Date: 2016-09-02
• Subjects: Geographic object-based image analysis -- multiresolution image segmentation -- very-high-resolution images -- segmentation by weighted aggregation; Geodesy -- Periodicals; Cartography -- Periodicals; Aerial photogrammetry -- Periodicals; Remote sensing -- Periodicals; 526.05
• DOI: 10.1080/15481603.2016.1215769
• Languages: English
• ISSNs: 1548-1603

Abstract

Abstract : Multiresolution segmentation (MRS) algorithm has been widely used to handle very-high-resolution (VHR) remote sensing images in the past decades. Unfortunately, segmentation quality is limited by the dependency of parameter selection on users' experience and diverse images. Contrarily, the segmentation by weighted aggregation (SWA) can partly overcome the above limitations and produce an optimal segmentation for maximizing the homogeneity within segments and the heterogeneity across segments. However, SWA is solely tested and justified with digital photos in computer vision field instead of VHR images. This study aims at evaluating SWA performance on VHR imagery. First, multiscale spectral, shape, and texture features are defined to measure homogeneity of image objects for segmentation. Second, SWA is implemented to handle QuickBird, unmanned aerial vehicle (UAV), and GF-1 VHR images and further compared with MRS in eCognition software to demonstrate the applicability of SWA to diverse images in building, vegetation and water, forest stands, farmland, and mountain areas. Third, the results are fully evaluated with quantitative measurements on segmented objects and classification-based accuracy assessment on geographic information system vector data. The results indicate that SWA can produce higher quality segmentations, need fewer parameters and manual interventions, create fewer segmentation levels, incorporate more features, and obtain larger classification accuracy than MRS.