Parameter evaluation and optimization for multi-resolution segmentation in object-based shadow detection using very high resolution imagery. Issue 12 (2nd December 2017)

• Record Type: Journal Article
• Authors: Luo, Hui; Li, Deren; Liu, Chong
• Is Part Of: Geocarto international. Volume 32:Issue 12(2017)
• Page Start: 1307
• Page End: 1332
• Publication Date: 2017-12-02
• Subjects: Parameter evaluation and optimization -- interaction effect -- orthogonal experimental design -- object-based shadow detection -- multi-resolution segmentation; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2016.1222632
• Languages: English
• ISSNs: 1010-6049

Abstract

Abstract: Object-based shadow detection in urban areas is an important topic in very high resolution remote sensing image processing. Multi-resolution segmentation (MRS) is an effective segmentation method, and is used for object-based shadow detection. However, several input parameters within MRS may result in unstable performance for final shadow detection; thus, the evaluation and optimization for the parameters upon the final shadow detection accuracy cannot be overlooked. In this paper, the three parameters in MRS (scale s, weight of colour w color and weight of compactness w compact ) upon the final result of a recently proposed method, object-based shadow detection with Dempster–Shafer theory, were evaluated and optimized by sensitivity analysis and Taguchi's method with three experimental data. Experiments show that scale s is the most sensitive parameter among the three parameters within MRS. More importantly, according to the Taguchi's method theory, there is a very significant interaction effect between s and w color, which cannot be overlooked. The shadow detection accuracy yielded by the optimum parameter combination in consideration of the interaction effect is higher than that only optimized by covering the main effect of single parameter in most cases.