Sikdar, A and Udupa, S and Sundaram, S and Sundararajan, N (2022) Fully Complex-valued Fully Convolutional Multi-feature Fusion Network(FC2MFN) for Building Segmentation of InSAR images. In: 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022, 4-7 December 2022, Singapore, pp. 581-587.
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Abstract
Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance. Although complex-valued deep learning networks perform better than their real-valued counterparts for complexvalued SAR data, phase information is not retained throughout the network, which causes a loss of information. This paper proposes a Fully Complex-valued, Fully Convolutional Multifeature Fusion Network (FC2MFN) for building semantic segmentation on InSAR images using a novel, fully complex-valued learning scheme. FC2 MFN learns multi-scale features, performs multi-feature fusion, and has a complex-valued output. For the particularity of complex-valued InSAR data, a new complexvalued pooling layer is proposed that compares complex numbers considering their magnitude and phase. This helps the network retain the phase information even through the pooling layer. Experimental results on the simulated InSAR dataset 1 show that FC2MFN achieves better results compared to other state-of theart methods in terms of segmentation performance and model complexity. © 2022 IEEE.
| Item Type: | Conference Paper |
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| Publication: | Proceedings of the 2022 IEEE Symposium Series on Computational Intelligence, SSCI 2022 |
| Publisher: | Institute of Electrical and Electronics Engineers Inc. |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Complex networks; Convolution; Convolutional neural networks; Deep learning; Image fusion; Radar imaging; Semantic Web; Semantics; Synthetic aperture radar, Complex-valued; Convolutional neural network; Fully complex-valued convolutional neural network; High resolution; Interferometric synthetic aperture radar; Interferometric synthetic aperture radars; Multi-feature fusion; Phase information; Semantic segmentation; Synthetic aperture radar images, Semantic Segmentation |
| Department/Centre: | Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering) |
| Date Deposited: | 25 Feb 2023 08:35 |
| Last Modified: | 25 Feb 2023 08:35 |
| URI: | https://eprints.iisc.ac.in/id/eprint/80710 |
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