With the advent of very high spatial resolution (VHR) satellite, spatial details within the image scene have increased considerably. This led to the development of object-based image analysis (OBIA) for the analysis of VHR satellite images. Image segmentation is the fundamental step for OBIA. However, a large number of techniques exist for RS image segmentation. To identify the best ones for VHR imagery, a comprehensive literature review on image segmentation is performed. Based on that review, it is found that the multiresolution segmentation, as implemented in the commercial software eCognition, is the most widely-used technique and has been successfully applied for wide variety of VHR images. However, the multiresolution segmentation suffers from the parameter estimation problem. Therefore, this study proposes a solution to the problem of the parameter estimation for improving its efficiency in VHR image segmentation.
The solution aims to identify the optimal parameters, which correspond to optimal
segmentation. The solution to the parameter estimation is drawn from the Equations
related to the merging of any two adjacent objects in multiresolution segmentation. The
solution utilizes spectral, shape, size, and neighbourhood relationships for a supervised solution. In order to justify the results of the solution, a global segmentation accuracy evaluation technique is also proposed. The solution performs excellently with the VHR images of different sensors, scenes, and land cover classes.
In order to justify the applicability of solution to a real life problem, a building
detection application based on multiresolution segmentation from the estimated
parameters, is carried out. The accuracy of the building detection is found nearly to be
eighty percent. Finally, it can be concluded that the proposed solution is fast, easy to
implement and effective for the intended applications.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:UNB.1882/35388 |
| Date | 28 September 2011 |
| Creators | Dey, Vivek |
| Contributors | University of New Brunswick, Faculty of Engineering, Zhang, Y., Zhong, M. |
| Publisher | Fredericton: University of New Brunswick |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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