TY - GEN
T1 - Sea-Land Clutter Segmentation Algorithm Based on Multi-measure Fusion with SVM Classifier
AU - Li, Kexin
AU - Shan, Tao
AU - Zhang, Yushi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6/4
Y1 - 2021/6/4
N2 - Effectively segmenting sea clutter and land clutter in the sea-land junction area is of great significance for target detection and recognition on the sea surface. Existing sea-land clutter segmentation algorithms are mostly based on a single measure, of which the segmentation effect is not very satisfactory. In view of this problem, this paper proposes a novel sea-land clutter segmentation algorithm based on multi-measure fusion. Firstly, the characteristics of the clutter in the echo data collected by the sea detection radar are analyzed, and multiple appropriate segmentation measures are selected as feature vectors and fed into the Support Vector Machine (SVM) classifier. Then the classification result is converted into a binary image and processed by morphological filtering method to ensure the connectivity between the sea clutter area and the land clutter area. Finally, the feasibility and validity of the algorithm are verified by the real radar data.
AB - Effectively segmenting sea clutter and land clutter in the sea-land junction area is of great significance for target detection and recognition on the sea surface. Existing sea-land clutter segmentation algorithms are mostly based on a single measure, of which the segmentation effect is not very satisfactory. In view of this problem, this paper proposes a novel sea-land clutter segmentation algorithm based on multi-measure fusion. Firstly, the characteristics of the clutter in the echo data collected by the sea detection radar are analyzed, and multiple appropriate segmentation measures are selected as feature vectors and fed into the Support Vector Machine (SVM) classifier. Then the classification result is converted into a binary image and processed by morphological filtering method to ensure the connectivity between the sea clutter area and the land clutter area. Finally, the feasibility and validity of the algorithm are verified by the real radar data.
KW - multi-measure fusion
KW - radar echo data
KW - sea-land clutter segmentation
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85114460110&partnerID=8YFLogxK
U2 - 10.1109/ICCSN52437.2021.9463606
DO - 10.1109/ICCSN52437.2021.9463606
M3 - Conference contribution
AN - SCOPUS:85114460110
T3 - 2021 13th International Conference on Communication Software and Networks, ICCSN 2021
SP - 94
EP - 98
BT - 2021 13th International Conference on Communication Software and Networks, ICCSN 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Communication Software and Networks, ICCSN 2021
Y2 - 4 June 2021 through 7 June 2021
ER -