IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Linear Feature Extraction Using a 2-D Random Field Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of Straight Lines in Aerial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Perceptual Organization and Visual Recognition
Perceptual Organization and Visual Recognition
Automatic Extraction of Man-Made Objects from Aerial and Space Images
Automatic Extraction of Man-Made Objects from Aerial and Space Images
A Level Set Method for the Extraction of Roads from Multispectral Imagery
AIPR '02 Proceedings of the 31st Applied Image Pattern Recognition Workshop on From Color to Hyperspectral: Advancements in Spectral Imagery Exploitation
Towards Knowledge-Based Extraction of Roads from 1m-Resolution Satellite Images
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Segmentation of High Resolution Satellite Images by Direction and Morphological Filters
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
International Journal of Computer Applications in Technology
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Extracting roads from satellite images is an important task in both research and practice. This work presents an improved model for road detection based on the principles of perceptual organization and classification fusion in human vision system (HVS). The model consists of four levels: pixels, primitives, structures and objects, and two additional sub-processes: automatic classification of road scenes and global integration of multiform roads. Based on the model, a novel algorithm for detecting roads from satellite images is also proposed, in which two types of road primitives, namely blob-like primitive and line-like primitive are defined, measured, extracted and linked using different methods for dissimilar road scenes. A hierarchical search strategy driven by saliency measurement is adopted in both linking processes. The blob primitives are linked using heuristic grouping and the line primitives are connected through genetic algorithm (GA) evolution. Finally, all of the linked road segments are normalized with centre-main lines and integrated into global smooth road curves through tensor voting. Experimental results show that the algorithm is capable of detecting multiform roads from real satellite images with high adaptability and reliability.