摘要
在分析应用对象化分析方法改进高空间分辨率遥感影像分类技术的基础上,提出了应用多层次的迭代模型改进分类流程,在自适应的迭代过程中有效地结合主导类别选择、高级对象特征计算、基于互信息的特征选择等技术提高对象化方法中丰富的影像特征的利用效率,同时,有机结合像素级特征信息弥补对象化特征。通过对SPOT5影像与航空影像两种数据源的土地覆盖分类的实验表明,该方法可以有效地提高分类精度。
While spectral analysis of images has yielded satisfactory results,they may not be enough to extract features from high spatial resolution satellite data.A novel iterative classification approach is proposed,which based on object-oriented technologies.This approach makes use of both pixel spectral features and object features through an iterative model to improve the accuracy of classification.Some adaptive techniques are used for each iteration,which are leading class selection,advanced object-based feature calculating,feature selection based on mutual information.The experiment of land cover classification on SPOT5 image and aerial photography shows this approach produces higher classification accuracy than normal object-oriented classification.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2011年第10期1154-1158,共5页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(60873033
40971228
40871203)
国家863计划资助项目(2009AA12Z123
2009AA12Z148)
浙江省自然科学基金杰出青年团队资助项目(R1090569)
关键词
对象化
分层分类
迭代模型
最大互信息
object-oriented
hierarchical classification
iterative model
max mutual information
