Abstract
Decision tree method has been applied to POLSAR image classification, due to its capability to interpret the scattering characteristics as well as good classification accuracy. Compared with popular machine learning classifiers, decision tree approach can explain the scattering process of certain type of targets by use of the polarimetric features at the tree nodes. Except the interpretability, decision tree approach could be transplanted to other data set without training process for the same terrain types, since the polarimetric features are inherently connected to the physical scattering properties. Currently, decision tree based classifiers, typically employ one single polarimetric feature at the nodes of the tree. The idea to increase the number of the polarization features at the decision tree node is expected to improve the classification result, which combine two or more polarimetric features to form a two or high dimension feature space. In this way, the classes which cannot be discriminated with one feature could possibly be separated with the space constructed by several features. However, it also inevitably leads to an increase in the computational burden. In fact, not all nodes require very high-dimensional feature space to achieve high classification precision. Therefore, in this article we proposed that the dimension of the feature space used in the decision tree nodes is adaptively changed from one to three, due to the separability of the classes under this node. The developed classification method is examined by the classical AIRSAR data in Flevoland area of the Netherlands, as well as GaoFen-3 data in Hulunbuir of China. The experiments show that the classification performance is superior to the fixed dimension feature decision tree methods, with less and reasonable computation time. Besides, the transferability of polarimetric features obtained by decision tree is preliminarily demonstrated in the application to another AIRSAR data.
Highlights
Polarimetric Synthetic Aperture Radar (POLSAR) is a multiparameter, multi-channel microwave imaging radar system, which is widely used in vegetation distribution [1], ocean research [2], [3], disaster assessment [4], [5] and so on
The main contribution of this article is to develop a new decision tree method for POLSAR data classification, in which the dimension of polarimetric feature space is adaptively decided at the tree nodes
FINE-GRAINED INTERPRETABILITY The decision tree classification method is different from other POLSAR image classification methods, for it retains the polarimetric features used in tree nodes which can be used to describe target scattering mechanism and interpret classification rules
Summary
Polarimetric Synthetic Aperture Radar (POLSAR) is a multiparameter, multi-channel microwave imaging radar system, which is widely used in vegetation distribution [1], ocean research [2], [3], disaster assessment [4], [5] and so on. The differences of polarimetric features are used to classify targets in these supervised classification methods, which are data-driven, the scattering characteristics of targets are not mapped to the certain features Compared to those popular machine learning and deep learning methods, the classical decision tree approach has its own advantages over several aspects. The main contribution of this article is to develop a new decision tree method for POLSAR data classification, in which the dimension of polarimetric feature space is adaptively decided at the tree nodes. By the use of adaptive dimensional feature space, the better features for discriminating certain class groups are founded It achieves the grained interpretation of similar scattering mechanisms of terrain types, and is preliminarily demonstrated by transplanting the features obtained at the tree nodes directly to another data sets of the same sensor for classification without training process.
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