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An iterative PolSAR image classification method with utilizing scattering and contextual information

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Abstract

Polarimetric synthetic aperture radar (PolSAR) images with multiple polarimetric channels have high discrimination ability. So, they are appropriate data for classification applications. The suitable use of both scattering (polarimetric) and spatial features in a PolSAR image is important to provide an accurate classification map. Many basic classifiers such as support vector machine (SVM) only works based on feature vectors and ignore the spatial features. To convert a basic classifier such as SVM to a powerful classifier for PolSAR image classification, the polarimetric and spatial base iterative (PSI) classifier is proposed in this work. The PSI method tries to refine the labels of an initial classification map obtained by a simple classifier (SVM in this work). To this end, three terms of similarity based on spatial, polarimetric and class-specific features are computed in neighborhood regions. According to the computed similarity values, the new labels are generated and the scattering features of the PolSAR images are refined. This simple process is repeated several times to a pre-determined number of iterations. The result is a clean and accurate classification map. The experiments are done on two AIRSAR PolSAR images. Compared to the base classifier (SVM), the proposed method provides about 10% and 6% increase in classification accuracy of Flevoland and Sanfrancisco datasets with 19 and 50 iterations, respectively. Moreover, the proposed PSI method shows desirable performance compared to several complex and powerful classification methods.

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Data availability

No new datasets were generated in this paper. The datasets used for the experiments are publicly available datasets.

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  1. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Maryam Imani

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Imani, M. An iterative PolSAR image classification method with utilizing scattering and contextual information. Multimed Tools Appl 83, 16605–16621 (2024). https://doi.org/10.1007/s11042-023-16205-z

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