TY - GEN
T1 - A Level Set Based Method for Land Masking in Ship Detection Using SAR Images
AU - Wang, Ziwei
AU - Yang, Wei
AU - Chen, Jie
AU - Li, Chunsheng
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - This paper presents an efficient approach to obtain land masking using synthetic aperture radar (SAR) images, which is based on level set method and fully convolutional network (FCN) classification. First, the level set method is applied to the cropped SAR input image for initial contours. Second, FCN model has been trained to classify the input image by two labels of water and land which will find the possible region existing real coastlines, named region of interest (RIO). Third, to select the desired contour from extracted boundaries in the first step, according to the proportions to be covered in RIO of step two. Then, final land masking can be obtained after morphological processing and color filling. The method proposed in this paper is fast and accurate enough for ship detection in high-resolution SAR images. It is also robust to speckle noise and geographical changes. Experimental results on GF-3 SAR images show good performance of this method.
AB - This paper presents an efficient approach to obtain land masking using synthetic aperture radar (SAR) images, which is based on level set method and fully convolutional network (FCN) classification. First, the level set method is applied to the cropped SAR input image for initial contours. Second, FCN model has been trained to classify the input image by two labels of water and land which will find the possible region existing real coastlines, named region of interest (RIO). Third, to select the desired contour from extracted boundaries in the first step, according to the proportions to be covered in RIO of step two. Then, final land masking can be obtained after morphological processing and color filling. The method proposed in this paper is fast and accurate enough for ship detection in high-resolution SAR images. It is also robust to speckle noise and geographical changes. Experimental results on GF-3 SAR images show good performance of this method.
KW - Land masking
KW - SAR image
KW - fully convolutional network
KW - level set method
UR - https://www.scopus.com/pages/publications/85077676274
U2 - 10.1109/IGARSS.2019.8898068
DO - 10.1109/IGARSS.2019.8898068
M3 - 会议稿件
AN - SCOPUS:85077676274
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3888
EP - 3891
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -