Abstract
Polarimetric Synthetic Aperture Radar (PolSAR) images have attracted much attention with abundant polarimetric scattering information. In recent years, many deep learning models have been proposed and highly expanded to PolSAR image classification. However, how to learn the complex matrix information is an indispensable problem. To address this problem, a Wishart Deeplab network is developed, which can not only learn the polarimetric matrix structure by designing Wishart network level but also learn multi-scale polarimetric scattering features by incorporating dilated convolution. Specifically, the Wishart-Deeplab based PolSAR classification model is constructed by designing the Wishart convolution operation to learn the statistical information of PolSAR data. Then, a deeplabV3 + network is followed to obtain the multi-scale semantic features by dilated convolution. By this way, statistical distrubtion-based and high-level semantic features are adaptively learned to improve the performance of the network. The experiments are conducted on two sets of real PolSAR data and results show that this method can obtain better classification results.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China under Grant 62006186, the Science and Technology Program of Beilin District in Xian under Grant GX2105, in part by the Open fund of National Key Laboratory of Geographic Information Engineering under Grant SKLGIE2019-M-3–2.
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Shi, J., He, T., Jin, H., Wang, H., Xu, W. (2023). Wishart Deeplab Network for Polarimetric SAR Image Classification. In: Yu, Z., Hei, X., Li, D., Song, X., Lu, Z. (eds) Intelligent Robotics. CIRAC 2022. Communications in Computer and Information Science, vol 1770. Springer, Singapore. https://doi.org/10.1007/978-981-99-0301-6_8
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