In order to improve the classification accuracy of polarimetric synthetic aperture radar (SAR) images, a two-level classification structure based on AdaBoost improved random forest (RF) and support vector machine (SVM) is proposed. Firstly, the AdaBoost improved RF (ADA_RF) is taken as the first-level classifier, which can assign weights according to the classification abilities of the decision trees. ADA_RF assigns high weights to strong decision trees. The first-level classifier can also assess the importance of input features and compute a ranking list. Feature selection can be conducted according to the list. The SVM classifier is trained with the selected features to predict the second-level classification result. Finally, the neighborhood voting method is used to fuse the results. The comparison experiments of AIRSAR polarization data shows that the classification structure can effectively improve the classification accuracy of polarimetric SAR images.
ZHANG Zheng
,
LI Shiqiang
. Polarimetric SAR image classification based on AdaBoost improved random forest and SVM[J]. Journal of University of Chinese Academy of Sciences, 2022
, 39(6)
: 776
-782
.
DOI: 10.7523/j.ucas.2021.0020
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