Sea-Surface Floating Small Target Detection by One-Class Classifier in Time-Frequency Feature Space

• Published in: IEEE transactions on geoscience and remote sensing Vol. 56; no. 11; pp. 6395 - 6411
• Main Authors: Shi, Sai-Nan, Shui, Peng-Lang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Amplitude; Amplitudes; Classifiers; Clutter; Computation; Detection; Detectors; Doppler effect; Doppler sonar; False alarms; Fast convex hull learning algorithm; Feature extraction; feature-based detection; Floating; floating small target detection; Floating structures; Image processing; Integration; Machine learning; normalized smoothed pseudo-Wigner–Ville distribution (SPWVD); one-class classifier; Radar; Radar cross-sections; Regions; Sensors; Surface clutter; Target detection; Temperature (air-sea); Time series; Time series analysis
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2018.2838260

This paper presents one feature-based detector to find sea-surface floating small targets. In integration time of the order of seconds, target returns exhibit time-frequency (TF) characteristics different from sea clutter. The normalized smoothed pseudo-Wigner-Ville distribution (SPWVD) is proposed to enhance TF characteristics of target returns, which is computed from the SPWVDs of time series at the cell under test (CUT) and reference cells around the CUT. The differences between target returns and the TF pattern of sea clutter are congregated on the normalized SPWVD. From that the ridge integration (RI) is computed and significant TF points from each time slice form a binary image. The number of connected regions and the maximum size of connected regions in the binary image are extracted and are combined with the RI into a 3-D feature vector. Due to the unavailability of the feature vector samples of radar returns with target, a one-class classifier with a controllable false alarm rate is constructed from the feature vector samples of sea clutter by the fast convex hull learning algorithm. As a result, a new feature-based detector is designed. It is compared with the tri-feature-based detector using amplitude and Doppler features and the fractal-based detector using the Hurst exponent of amplitude time series on the recognized IPIX radar database for floating small target detection. The results show that a significant improvement in detection performance is attained.