国土资源遥感2018，Vol.30Issue(1)：37-44,8.DOI:10.6046/gtzyyg.2018.01.06
基于超像素MRF的农田地区高分遥感影像分割
High resolution remote sensing image segmentation using super-pixel MRF for agricultural area
摘要
Abstract
In view of the problem that the traditional super-pixel Markov random field(MRF) image segmentation model cannot fully utilize spatial context information,a new super-pixel MRF model is proposed. This algorithm incorporates higher-order neighborhood model into the interactive potential term of MRF. The new model enables the interactive potential to fully exploit the spatial context information contained in the super-pixel neighborhood system. Additionally,a new class-wise estimation method for β is proposed,which is based on norm distance. By utilizing two scenes of high - resolution remote sensing images acquired over different agricultural landscapes, validation experiment was conducted. The experiment results indicate that the proposed method can better use the contextual information such as edge strength,thus achieving higher segmentation accuracy. Moreover,the algorithm proposed by the authors showed superior performance when it was compared with other super-pixel MRF approa-ches.关键词
超像素/马尔科夫随机场/高阶邻域/农田地区Key words
super-pixel/Markov random field (MRF)/higher-order neighborhood/agricultural area分类
信息技术与安全科学引用本文复制引用
苏腾飞,张圣微,李洪玉..基于超像素MRF的农田地区高分遥感影像分割[J].国土资源遥感,2018,30(1):37-44,8.基金项目
国家自然科学基金项目"内蒙古典型草原水文过程及其扰动与触发草地退化的水文临界条件实验与模拟研究"(编号:51269014)、"科尔沁沙地典型生态系统水热通量传输机理及其与植被耦合关系试验和模拟研究"(编号:51569017)、"面向对象的河套灌区遥感作物分类算法研究"(编号:61701265)、内蒙古自然科学基金项目"半干旱区沙地典型生态系统水热通量传输机理研究"(编号:2015MS0514)和中国博士后科学基金面上资助项目"西部地区博士后人才资助计划"(编号:2015M572630XB)共同资助. （编号:51269014）
