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Multi-Spectral Source-Segmentation Using Semantically-Informed Max-Trees
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AI
DOI：10.1109/ACCESS.2024.3403309.png)
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摘要
En 中文
In this paper, we propose an innovative approach to multi-band source-segmentation that addresses the constraints of single-band max-tree-based methods and effectively manages component-graph complexity. Our method extends multiple max-trees by integrating semantically meaningful nodes, derived from statistical tests, into a structured graph. This integration enables the exploration of correlations among cross-band emissions, enhancing segmentation accuracy. Evaluation with artificial multi-band astronomical images shows our method's superior accuracy in detecting and segmenting multi-spectral imagery. We achieve 98% accuracy in identifying correlated cross-band sources. Compared to state-of-the-art methods, our approach improves detection precision from 0.92 to 0.95 without sacrificing recall. Furthermore, quantitative analysis demonstrates significant speed enhancements, particularly on 3-channel images sized at 1,000 pixels squared, our method achieves up to an approximately $31\times $ acceleration when compared to a component-graph-based approach. The versatility and effectiveness of the proposed method suggest applications in remote sensing and multi-spectral large-scale image data analysis.
Keyword:
Image segmentation
Heuristic algorithms
Object recognition
Data structures
Brightness
Time complexity
Statistical analysis
Hierarchical systems
Hierarchical structures
connected components
multi-band source-segmentation
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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