Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty
Abstract
:1. Introduction
2. Study Area and Data
Image | Platform | Size | Spatial Resolution | Position | Code |
---|---|---|---|---|---|
a | WorldView-2 | 872 × 896 | 0.6 m | Hanzhong | R1 |
b | Aerial plane | 835 × 835 | 1 m | Three Gorges | R2 |
c | RapidEye | 622 × 597 | 5 m | Miyun | R3 |
3. Methodology
3.1. Initial Segmentation
3.2. RAG and NNG Constructing and Region Merging
3.2.1. RAG and NNG Construction
3.2.2. Region Merging
Merging Criterion
Merging Strategy
3.3. Minor Object Elimination
3.4. Quantitative Assessment Method
4. Results and Discussion
4.1. The Effect of Algorithm Parameters
4.1.1. Scale of Initial Segmentation
4.1.2. The Constraining Threshold for Object Size in CSVD: T
4.1.3. The Edge Penalty Control Variable ε
4.1.4. The Scale Parameter for MC
4.2. Comparison with eCognition Software Segmentation
5. Conclusions
Acknowledgment
Author Contributions
Conflicts of Interest
References
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Chen, B.; Qiu, F.; Wu, B.; Du, H. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty. Remote Sens. 2015, 7, 5980-6004. https://doi.org/10.3390/rs70505980
Chen B, Qiu F, Wu B, Du H. Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty. Remote Sensing. 2015; 7(5):5980-6004. https://doi.org/10.3390/rs70505980
Chicago/Turabian StyleChen, Bo, Fang Qiu, Bingfang Wu, and Hongyue Du. 2015. "Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty" Remote Sensing 7, no. 5: 5980-6004. https://doi.org/10.3390/rs70505980