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Ship Classification in SAR Images Using a New Hybrid CNN–MLP Classifier

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Abstract

Ship detection on the SAR images for marine monitoring has a wide usage. SAR technology helps us to have a better monitoring over intended sections, without considering atmospheric conditions, or image shooting time. In recent years, with advancements in convolutional neural network (CNN), which is one of the well-known ways of deep learning, using image deep features has increased. Recently, usage of CNN for SAR image segmentation has been increased. Existence of clutter edge, multiple interfering targets, speckle and sea-level clutters makes false alarms and false detections on detector algorithms. In this letter, constant false alarm rate is used for object recognition. This algorithm, processes the image pixel by pixel, and based on statistical information of its neighbor pixels, detects the targeted pixels. Afterward, a neural network with hybrid algorithm of CNN and multilayer perceptron (CNN–MLP) is suggested for image classification. In this proposal, the algorithm is trained with real SAR images from Sentinel-1 and RADARSAT-2 satellites, and has a better performance on object classification than state of the art.

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Acknowledgements

Funding was provided by Shahid Chamran University of Ahvaz (Grant No. 96/3/02/16670).

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Foroogh Sharifzadeh, Gholamreza Akbarizadeh & Yousef Seifi Kavian

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  1. Foroogh Sharifzadeh
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  2. Gholamreza Akbarizadeh
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  3. Yousef Seifi Kavian
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Correspondence to Gholamreza Akbarizadeh.

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Sharifzadeh, F., Akbarizadeh, G. & Seifi Kavian, Y. Ship Classification in SAR Images Using a New Hybrid CNN–MLP Classifier. J Indian Soc Remote Sens 47, 551–562 (2019). https://doi.org/10.1007/s12524-018-0891-y

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  • DOI: https://doi.org/10.1007/s12524-018-0891-y

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