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- Title
Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty.
- Authors
Bo Chen; Fang Qiu; Bingfang Wu; Hongyue Du
- Abstract
Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD) and the edge penalty (EP). First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition's multi-scale segmentation.
- Subjects
IMAGE segmentation; IMAGE analysis; MULTISCALE modeling; REMOTE sensing; CONSTRAINED optimization
- Publication
Remote Sensing, 2015, Vol 7, Issue 5, p5980
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs70505980