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Residual network based on entropy-anisotropy-alpha target decomposition for polarimetric SAR image classification

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Abstract

Convolutional residual networks have shown great success for classification of polarimetric synthetic aperture radar (PolSAR) images especially when the depth of network is increased and limited training samples are available. But, the ability of convolutional kernels is in extraction of spatial features from neighboring pixels. They may not sufficiently able to extract the radar’s physical features from the PolSAR images. To deal with this difficulty, a residual network is proposed in this work. In addition to the feature maps extracted by convolutional kernels of previous layers, the proposed network injects the physical feature maps extracted by the H-A-\(\mathrm{\alpha }\) decomposition to each addition layer. Moreover, the use of a convolutional autoencoder behind the residual blocks is proposed to reduce the speckle noise. The proposed method provides more accurate classification maps compared to conventional residual networks and several state-of-the-art methods. However, it needs more running time for implementation.

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Data availability

No new data is used in this paper. The datasets used for the experiments are benchmark datasets.

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Authors and Affiliations

  1. Image Processing and Information Analysis Lab, Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Amir Hossein Ghazvinizadeh, Maryam Imani & Hassan Ghassemian

Authors
  1. Amir Hossein Ghazvinizadeh
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  2. Maryam Imani
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  3. Hassan Ghassemian
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Contributions

Amir Hossein Ghazvinizadeh: Data Assessment and Experiments Discussion.

Maryam Imani: Development of Ideas, Conceptualization, Software, Writing, and Supervision.

Hassan Ghassemian: Review and Supervision.

Corresponding author

Correspondence to Maryam Imani.

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The authors declare that there is no financial or non-financial interests. There is no conflict of interest.

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Communicated by: H. Babaie

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Ghazvinizadeh, A.H., Imani, M. & Ghassemian, H. Residual network based on entropy-anisotropy-alpha target decomposition for polarimetric SAR image classification. Earth Sci Inform 16, 357–366 (2023). https://doi.org/10.1007/s12145-023-00944-6

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  • DOI: https://doi.org/10.1007/s12145-023-00944-6

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