학술논문
Real time SAR Ship Detection using novel SarNeDe method
Document Type
Conference
Author
Source
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS Geoscience and Remote Sensing Symposium IGARSS , 2021 IEEE International. :2198-2201 Jul, 2021
Subject
Language
ISSN
2153-7003
Abstract
Many research works using deep learning techniques for automatic ship detection from SAR images have good detection accuracy. But the main problem in these methods is false detection, mostly due to speckle presence. Therefore, we propose a new deep learning model with a novel SarNeDe preprocessing stage to address this problem. We are introducing a deep learning architecture to detect and localize ships in the SAR image. First, generate a three-channel SarNeDe image in the preprocessing step. Then, this SarNeDe image is used to train the model to predict the ship's position in the SAR image. We experimented on the public SAR ship detection dataset (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) to validate the proposed method's feasibility. We used python 3.5 for coding with the Keras framework in the NVIDIA Tesla K80 GPU hardware platform. The experimental results indicated that our proposed method's ship detection accuracy has increased with reduced false detection percentage.