TY - GEN
T1 - Ship detection based on deep convolutional neural networks for polsar images
AU - Zhou, Feng
AU - Fan, Weiwei
AU - Sheng, Qiangqiang
AU - Tao, Mingliang
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - In this paper, we proposed a ship detection method based on deep convolutional neural networks for PolSAR images. The proposed ship detector firstly segments PolSAR images into sub-samples using a sliding window of fixed size to effectively extract translational-invariant spatial features. Further, the modified faster region based convolutional neural network (Faster-RCNN) method is utilized to realize ship detection for ships with different sizes and fusion the detection result. Finally, the proposed method was validated using real measured NASA/JPL AIRSAR datasets by comparing the performance with the modified constant false alarm rate (CFAR) detector. The comparison results demonstrate the validity and generality of the proposed detection algorithm.
AB - In this paper, we proposed a ship detection method based on deep convolutional neural networks for PolSAR images. The proposed ship detector firstly segments PolSAR images into sub-samples using a sliding window of fixed size to effectively extract translational-invariant spatial features. Further, the modified faster region based convolutional neural network (Faster-RCNN) method is utilized to realize ship detection for ships with different sizes and fusion the detection result. Finally, the proposed method was validated using real measured NASA/JPL AIRSAR datasets by comparing the performance with the modified constant false alarm rate (CFAR) detector. The comparison results demonstrate the validity and generality of the proposed detection algorithm.
KW - Polarimetric synthetic aperture radar (PolSAR)
KW - Ship detection
KW - Terms-Deep convolutional neural networks
UR - http://www.scopus.com/inward/record.url?scp=85063985109&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8518589
DO - 10.1109/IGARSS.2018.8518589
M3 - 会议稿件
AN - SCOPUS:85063985109
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 681
EP - 684
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -