用于SAR图像海面溢油自动识别的Bagging-AdaBoost决策树分类器系统

A Bagging-Ada Boost-DT Multiple Classifier System Based on SAR Image for Marine Oil Spill Automatic Detection

目前应用于SAR图像海面溢油检测的分类器大多为单分类器,其检测率往往难以达到要求。本文引入AdaBoost算法和Bagging算法,内核采用决策树分类器(Decision tree,DT),形成两种分类器系统DT-A(AdaBoost-Decisiontree)和DT-B(Bagging-Decision-tree)。进而提出以AdaBoost分类器系统为内核的Bagging系统DT-AB(Bagging-AdaBoost-Decision-tree)。对1 448个Envisat/ASAR海面暗目标样本进行分类实验,DT-AB分类器系统与DT单分类器相比,溢油暗目标检测率从74%增至83%,错检率从26%降至16%。对4种分类器的检测率、泛化性能和稳定性能进行了比较测试,结果如下:(1)DT-A和DT-B的泛化性能明显优于DT,并且DT-A的泛化性能略优于DT-B;(2)DT-A和DT-B比DT的稳定性能提高一倍以上,DT-B的稳定性能略优于DT-A;(3)DT-AB的泛化性能和稳定性能均优于DT-A和DT-B。因此,DT-AB分类器系统用于卫星SAR图像海面溢油自动识别业务化系统具有应用前景。

Most classifiers applied to oil detection by SAR images are kinds of single classifier so far,whose detection rates are often hard to meet the requirements.Two multiple classifier systems,DT-A(AdaBoost-Decision-tree)and DT-B(Bagging-Decision-tree),which are composed of decision tree(DT)classifiers with Adaboost and Bagging algorithms,respectively,are investigated for SAR oil detection.A Bagging classifier system DT-AB(Bagging-AdaBoost-Decision-tree)is further proposed which takes DT-A as its core classifier.Experiments with 1 448 dark targets extracted from Envisat/ASAR show that DT-AB significantly improves the classify performance comparing to DT.The detection rate increases from 74%to 83%and the false discovery rate decreases from 26%to 16%.Using the same sample set,many tests are implemented to compare the detection rate、generalization performance and stability of the four kinds of classifiers,DT,DT-A,DT-B and DT-AB.The results are summarized as follows:(1)for generalization performance,both DT-A and DT-B are much better than DT and DT-A is slightly better than DT-B;(2)for stability,both DT-A and DT-B are more than two times of DT-B and DT-B is slightly better than DT-A;(3)for the generalization performance and the stability,DT-AB is better than both DT-A and DT-B.Therefore,the multiple classifier system DT-AB has application potential in an operational automatic detection system for marine oil spill detection by SAR images.