It is always a challenging problem for marine surface surveillance radar to detect sea-surface floating small targets. Conventional detectors using incoherent integration and adaptive clutter suppression have low detection probabilities for such targets with weak returns and unobservable Doppler shifts. In this paper, three features of a received vector at a resolution cell-the relative amplitude, relative Doppler peak height, and relative entropy of the Doppler amplitude spectrum-are exploited to give returns with targets from sea clutter. Real datasets show that each feature alone has some discriminability, and the three features jointly exhibit strong discriminability. Due to diversity of targets in practice, it is impossible to get features of returns with all kinds of targets. We recast detection of sea-surface floating small targets as a one-class anomaly detection problem in the 3D feature space. A fast convexhull learning algorithm is proposed to learn the decision region of the clutter pattern from feature vectors of clutter-only observations. As a result, a tri-feature-based detector is developed. The experiment results for the IPIX datasets show that the proposed detector at an observation time of several seconds attains better detection performance than several existing detectors.

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.

This paper proposes a corner detector and classifier using anisotropic directional derivative (ANDD) representations. The ANDD representation at a pixel is a function of the oriented angle and characterizes the local directional grayscale variation around the pixel. The proposed corner detector fuses the ideas of the contour- and intensity-based detection. It consists of three cascaded blocks. First, the edge map of an image is obtained by the Canny detector and from which contours are extracted and patched. Next, the ANDD representation at each pixel on contours is calculated and normalized by its maximal magnitude. The area surrounded by the normalized ANDD representation forms a new corner measure. Finally, the nonmaximum suppression and thresholding are operated on each contour to find corners in terms of the corner measure. Moreover, a corner classifier based on the peak number of the ANDD representation is given. Experiments are made to evaluate the proposed detector and classifier. The proposed detector is competitive with the two recent state-of-the-art corner detectors, the He & Yung detector and CPDA detector, in detection capability and attains higher repeatability under affine transforms. The proposed classifier can discriminate effectively simple corners, Y-type corners, and higher order corners.

Sea-surface small target detection is always a difficult problem in high-resolution maritime ubiquitous radars for complex characteristics of sea clutter, weak target returns, and diversity of targets. Multiple features extracted from radar returns in different domains have ability but not enough to solely distinguish radar returns with target from sea clutter. Joint exploitation of multiple features becomes the key to improve detection performance. In this paper, the KNN algorithm and anomaly detection idea are cooperated to develop a novel seasurface target detection method in the feature space spanned by the eight existing salient features. The detection is realized by the anomaly detection followed by a specially designed KNN-based classifier with a controllable false alarm rate. In the anomaly detection, a decision region is determined by the hyper-spherical coverage of the training set of sea clutter that is sufficient and ergodic in the feature space. The KNN-based classifier is designed based on the training sample set of sea clutter and the training sample set of simulated target returns plus sea clutter that is sufficient but non-ergodic, by joint usage of feature weighting, neighbour weighting, and distance weighting. The novel method is validated by the two open and recognized IPIX and CSIR radar databases for sea-surface small target detection. The results show that it provides significant performance improvement in comparison with the existing multiple-featurebased detection methods, owing to the fact that the novel method avoids the dimension restriction and feature compression loss in the existing methods.

By introducing Gaussian-Gamma-shaped (GGS) bi-windows instead of traditional rectangle bi-windows, a new ratio-based edge detector is proposed to extract thin edges of synthetic aperture radar (SAR) images. As poor 2-D smoothing filters, the rectangle window functions are shown to be apt to incur false-edge pixels near true edges. Using the GGS window functions reduces false-edge pixels near true edges, which can be verified by analyzing effective false maxima in the edge strength maps (ESMs). Operating the nonmaximum suppression and hysteresis thresholding on the ratio-based ESM using GGS bi-windows yields thin edges of SAR images. The receiver-operating-characteristic curve is used to evaluate edge detectors. The experimental results to a synthetic SAR image show that the detector using GGS bi-windows attains better performance than the one using rectangle bi-windows.