基于随机森林的地理要素面向对象自动解译方法

面向地理对象影像分析(GEOBIA)技术取得了显著的进展,代表了遥感影像解译的发展范式,其主要目标是发展智能化分析方法。随机森林机器学习方法是一种相对新的、数据驱动的非参数分类方法,具有自动特征优选、自动模型构建等优势,为智能化分析提供了有效手段。充分利用GEOBIA及随机森林机器学习的优势,提出了基于随机森林的地理要素面向对象自动解译方法,阐述了随机森林面向对象分类方法的技术流程,为设计和实现该方法提供了详细指导,有助于指导用户优选特征和构建分类模型。通过与支持向量机分类的对比实验证明,该方法可以自动进行特征优选及分类模型的构建,利用较少的特征得到较高的分类精度,在不损失性能的前提下减少了计算量和内存使用,能够为大范围、大区域地理要素自动解译提供先验知识及自动化的手段。

An Object-based Automatic Interpretation Method for Geographic Features Based on Random Forest Machine Learning

Geographic object-based image analysis (GEOBIA) techniques have recently seen considerable development in comparison to traditional pixel-based image analysis, representing a paradigm shift in remote sensing interpretation. The main aim is to incorporate and develop geographic-based intelligence. The random forest (RF) machine learning method is a relatively new, non-parametric, data-driven classification method that can supply intelligent means for feature selection and classification modelling. This paper presents a novel RF GEOBIA method for land-cover classification that makes full use of the advantages of GEOBIA and RF. A detailed RF GEOBIA workflow is proposed to guide the design and implementation of the method, and to guide experts during elaboration of feature selection and classification modelling. Theoretical and experimental results are compared with the support vector machine (SVM) approach, demonstrating that it is a robust and intelligent method for land-cover classification with wrapper feature selection and classification modelling. The RF GEOBIA method reduces the number of features required, computing time, and memory requirements, with no associated reduction in performance. It also provides a priori knowledge for further classification and supports large scale applications where \