Effective pixel classification of Mars images based on ant colony optimization feature selection and extreme learning machine

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• 作者：Abdolreza Rashno^a ; ^{a.rashno@ec.iut.ac.ir}Author Vitae ; Behzad Nazari^a ; ^{nazari@cc.iut.ac.ir}Author Vitae ; Saeed Sadri^a ; ^{sadri@cc.iut.ac.ir}Author Vitae ; Mohamad Saraee^b ; ^{m.saraee@salford.ac.uk}Author Vitae
• 关键词：Pixel classification ; Feature selection ; Ant colony optimization ; Extreme learning machine ; Wavelet features ; Color features
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：22 February 2017
• 年：2017
• 卷：226
• 期：Complete
• 页码：66-79
• 全文大小：2011 K
• 卷排序：226

文摘

one of the most important tasks of Mars rover, a robot which explores the Mars surface, is the process of automatic segmentation of images taken by front-line Panoramic Camera (Pancam). This procedure is highly significant since the transformation cost of images from Mars to earth is extremely high. Also, image analysis may help Mars rover for its navigation and localization. In this paper, a new feature vector including wavelet and color features for Mars images is proposed. Then, this feature vector is presented for extreme learning machine (ELM) classifier which leads to a high accuracy pixel classifier. It is shown that this system statistically outperforms support vector machine (SVM) and k-nearest neighbours (KNNs) classifiers with respect to both accuracy and run time. After that, dimension reduction in feature space is done by two proposed feature section algorithms based on ant colony optimization (ACO) to decrease the time complexity which is very important in Mars on-board applications. In the first proposed feature selection algorithm, the same feature subset is selected among the feature vector for all pixel classes, while in the second proposed algorithm, the most significant features are selected for each pixel class, separately. Proposed pixel classifier with complete feature set outperforms prior methods by 6.44% and 5.84% with respect to average Fmeasure and accuracy, respectively. Finally, proposed feature selection methods decrease the feature vector size up to 76% and achieves Fmeasure and accuracy of 91.72% and 91.05%, respectively, which outperforms prior methods with 87.22% and 86.64%.