Bio-inspired classifier for road extraction from remote sensing imagery

Xu, Jiawei, Wang, Ruisheng and Yue, Shigang (2014) Bio-inspired classifier for road extraction from remote sensing imagery. Journal of Applied Remote Sensing, 8 (1). 083577. ISSN 1931-3195

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An adaptive approach for road extraction inspired by the mechanism of primary visual cortex (V1) is proposed. The motivation is originated by the characteristics in the receptive field from V1. It has been proved that human or primate visual systems can distinguish useful cues from real scenes effortlessly while traditional computer vision techniques cannot accomplish this task easily. This idea motivates us to design a bio-inspired model for road extraction from remote sensing imagery. The proposed approach is an improved support vector machine (SVM) based on the pooling of feature vectors, using an improved Gaussian radial basis function (RBF) kernel with tuning on synaptic gains. The synaptic gains comprise the feature vectors through an iterative optimization process representing the strength and width of Gaussian RBF kernel. The synaptic gains integrate the excitation and inhibition stimuli based on internal connections from V1. The summation of synaptic gains contributes to pooling of feature vectors. The experimental results verify the correlation between the synaptic gain and classification rules, and then show better performance in comparison with hidden Markov model, SVM, and fuzzy classification approaches. Our contribution is an automatic approach to road extraction without prelabeling and postprocessing work. Another apparent advantage is that our method is robust for images taken even under complex weather conditions such as snowy and foggy weather. © 2014 SPIE.

Keywords:Hidden Markov models, Iterative methods, Radial basis function networks, Remote sensing, Support vector machines, Vectors, Automatic approaches, Feature vectors, Gaussian radial basis functions, Iterative Optimization, Primary visual cortex, Remote sensing imagery, Road classifications, Traditional computers, Feature extraction, NotOAChecked
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:14764
Deposited On:02 Apr 2015 11:50

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