Research of Water Hazard Detection Based on Color and Texture Features

In this paper, we focus on the need for water hazard detection based on the characteristics of the static water body in off-road environment, which includes three main sections: extraction of color and texture features, building SVM model and practical detection of water bodies. Based on the features of high intensity, low saturation and low texture of the water bodies existed in off-road environment. Saturation-value ratio feature extracted from hsv color space of water body region, combined with other four texture features conducted by gray level co-occurrence matrix constitute the five-feature vector. Training set is established from sample images after the images are well preprocessed. Then build the svm model based on the training set. Our task is to separate practical samples into two classes: water region and land region according to the predict result calculate by svm model. Experimental results demonstrate significant progress on detection of water body hazard in off-road environment, which effectively reduce the influence of illumination variation exert on detection when only using color feature to detect. Copyright © 2013 IFSA.

[1]  Larry Matthies,et al.  Daytime water detection based on color variation , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  F. Ulaby,et al.  Textural Infornation in SAR Images , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Andres Huertas,et al.  Daytime Water Detection by Fusing Multiple Cues for Autonomous Off-Road Navigation , 2006 .

[4]  Larry H. Matthies,et al.  Daytime water detection based on sky reflections , 2011, 2011 IEEE International Conference on Robotics and Automation.

[5]  Fabrice Meriaudeau,et al.  A SURVEY ON OUTDOOR WATER HAZARD DETECTION , 2009 .

[6]  Peng Hong Performance Evaluation for the Algorithms to Measure Texture Coarseness , 2011 .

[7]  Shane Brennan,et al.  Evaluating the performance of unmanned ground vehicle water detection , 2010, PerMIS.

[8]  Chen Chang-feng Experimental Studies of Several Reflection Detection Methods , 2011 .

[9]  Zhiyu Xiang,et al.  Multi-Feature Fusion Based Outdoor Water Hazards Detection , 2007, 2007 International Conference on Mechatronics and Automation.

[10]  Ovidiu Ivanciuc,et al.  Applications of Support Vector Machines in Chemistry , 2007 .