Ship Detection of SAR Image in Complex Nearshore Environment | IEEE Conference Publication | IEEE Xplore

Ship Detection of SAR Image in Complex Nearshore Environment


Abstract:

Synthetic aperture radar (SAR) ship detection in complex near-shore environment remains a challenging task for traditional deep learning models due to relatively serious ...Show More

Abstract:

Synthetic aperture radar (SAR) ship detection in complex near-shore environment remains a challenging task for traditional deep learning models due to relatively serious missed alarms. To achieve accurate detection of near-shore ships, this paper proposes a two-stage ship target detection algorithm based on Markov random fields and dual-attention network (MRF-DANet). In the first stage, the MRF-DANet proposes to extract the regions of interest (ROI) of ships by fusing multi-segmentations obtained by the MRF and Otsu algorithm, in which the MRF is used to implement the fine segmentation of the near-shore scene while the sea-land being divided by the Otsu. In this way, the MRF-DANet could effectively focus on the possible ships near the coastline and avoid missed alarms. Afterwards, a sequential dual-attention network is designed to extract discriminative deep features in both spatial and channel dimensions for the detection of nearshore ships. Experimental results on the GF-3 AIR-SARShip-1.0 dataset demonstrate that the proposed MRF-DANet performs better than the recent deep learning models in the nearshore ship detection.
Date of Conference: 11-13 December 2022
Date Added to IEEE Xplore: 10 January 2023
ISBN Information:
Conference Location: Singapore, Singapore

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