Integrated mixture model and ensemble learning geographic object-based image analysis for road network extraction

ABSTRACT

Delineation of road networks is often hindered by shadows, occlusions and spectrally similar objects. A hybrid Geographic object-based Image analysis (GeOBIA) technique that combines Mixture Model segmentation with Ensemble Learning algorithms to extract road networks is proposed. The Multispectral Local Dirichlet Mixture Model (MLDMM) highlights the built-up area. The bagging or subspace integrated ensemble learning-based classification  makes the framework immune to overfitting and a novel statistical feature selection phase boosts the performance by 18%. Post-processing by path morphology achieves complete networks. MLDMM-SDEC proffers a precision of 99.86%, a recall of 90.90%, an F1-score of 95.23%, a detection quality of 94.73%, and an accuracy of 96.77%.

KEYWORDS:

• Mixture model
• segmentation
• road extraction
• classification
• GeOBIA

Disclosure statement

No potential conflict of interest was reported by the author(s).