国防科技大学学报2017，Vol.39Issue(4)：77-86,10.DOI:10.11887/j.cn.201704012
高分辨率遥感影像的自优化迭代分类方法
Self-optimizing iterative classification method of high-resolution remote sensing images
摘要
Abstract
A self-optimizing iterative classification method based on image segments which classifies high-resolution remote sensing images by acquiring training samples through semi-supervised fuzzy C-means and designing the self-optimizing iterative classifier based on support vector machine was proposed.Image segments could be obtained by fractal net evolution approach and a few labeled samples were selected;based on labeled samples, image segments were clustered by semi-supervised fuzzy C-means clustering method and then training samples could be obtained by intensity filtration from clustering results;the self-optimizing iterative support vector machine was designed to carry on classification iteratively until the classification requirements were met and during the classification process, training samples were updated and optimized to improve the performance of the classifier by statistical analyses of the two adjacent classifications.QuickBird and WorldView images of Wuhan City were classified by the method proposed by this paper and the overall accuracy achieved 94.67% and 92%.In comparison with the overall accuracy of the classification with training samples selected by manual work, the regular support vector machine classification method and the least squares support vector machine classification method, the accuracy of the suggested method is obviously higher and the classification effect is better.关键词
高分辨率遥感影像/像斑/自优化/半监督/模糊C均值/支持向量机Key words
high-resolution remote sensing images/image segments/self-optimization/semi-supervised/fuzzy C-means/support vector machine分类
天文与地球科学引用本文复制引用
史蕾,万幼川,李刚,姜莹..高分辨率遥感影像的自优化迭代分类方法[J].国防科技大学学报,2017,39(4):77-86,10.基金项目
国家科技支撑计划资助项目(2014BAL05B07) （2014BAL05B07）
高等学校博士学科点专项科研基金资助项目(20130141130003) （20130141130003）
测绘遥感信息工程国家重点实验室开放基金资助项目(13R04) （13R04）
