摘要
针对高分辨率遥感图像分类中空间上下文信息表达的难题,提出了一种新的多尺度条件随机场(CRF)模型。首先将图像内容表示成从细到粗三个超像素层:区域层、对象层、场景层,并将超像素特征逐层关联形成特征向量;再利用支持向量机(SVM)定义CRF关联势函数,利用相邻超像素特征对比度加权的Potts模型定义CRF交互势函数,最后形成一个分层特征关联的多尺度SVM-CRF模型。以Quickbird遥感图像中两个复杂场景为测试数据对该模型的分类有效性进行了验证,结果表明:该模型比基于上述三个超像素层的单尺度SVM-CRF模型分类精度分别平均提高了2.68%、1.66%、3.75%,而且分类时耗时较少。
For the difficulty of expressing spatial context in classification of high resolution remote sensing imagery, a new multi-scale Conditional Random Field （CRF）model was proposed here. Specifically, a given image was represented as three superpixel layers respectively being region, object and scene from fine to coarse firstly. Then features were extracted layer-by- layer, and those features from the three layers were associated with each other to form a feature vector for each node in region layer. Secondly, Support Vector Machine （SVM） was adopted to define association potential function, and Potts model weighted by feature contrast function was used to define interaction potential function of CRF model, thus a layer-by-layer feature associative and multi-scale SVM-CRF model was formed. To confirm the effectiveness of the proposed model in classification, experiments on two complex scenes from Quickbird remote sensing imagery were developed. The results show that the proposed model achieves an improved accuracy averagely 2.68%, 2.37%, 3.75% higher than that of SVM-CRF model based on either region, object or scene layer, also it consumes less time in classification.
出处
《计算机应用》
CSCD
北大核心
2014年第6期1741-1745,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(41301386
41372330)
中央高校基本科研业务费专项资金资助项目(CHD2011JC085)
关键词
遥感图像分类
条件随机场
超像素
多尺度
支持向量机
remote sensing image classification
Conditional Random Field （CRF）
superpixel
multi-scale
SupportVector Machine （SVM）
