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14 December 2020 Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network
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Abstract

Park, W.; Baek, W.-K.; Won, J.-S., and Jung, H.-S., 2020. Comparison of input image size for ship detection from KOMPSAT-5 SAR image using deep neural networks (DNNs). In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 208-217. Coconut Creek (Florida), ISSN 0749-0208.

This study shows the results of ship detection in KOMPSAT-5 X-band SAR images using Deep Learning (DL) based the fast Deep Neural Network (DNN) method with the iterative kernel-based false alram detection algorithm. In addition, this study verifies the detection accuracy according to the size of the input data for are typical errors in SAR images such as backscattering noise, side-lobe, and ghost effect. The test results of the Fast DNN method confirmed that the error caused by the back scattering noise and side-lobe decreased as the input data size increased, however, the error for the ghost effect did not improve significantly. Also, the False Alarm Rate (FAR) was greatly improved using the iterative kernel-based false alram detection algorithm. However, caution is needed when detecting small ships because very small boats disappear. The results of this study showed that the size of the input data was very effective at 51 × 51 or higher when the fast DNN method and the iterative kernel-based false alram detection algorithm were applied to the KOMPSAT-5 images to detect ships, allowing the FAR and Intersaction over Union (IoU) showing relatively high accuracy at 0.270 and 0.611.

©Coastal Education and Research Foundation, Inc. 2020
Wook Park, Won-Kyung Baek, Joong-Sun Won, and Hyung-Sup Jung "Comparison of Input Image Dimensions for Ship Detection from KOMPSAT-5 SAR Image Using Deep Neural Network," Journal of Coastal Research 102(sp1), 208-217, (14 December 2020). https://doi.org/10.2112/SI102-026.1
Received: 28 September 2020; Accepted: 11 November 2020; Published: 14 December 2020
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