English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

In this letter, we consider the varying detection environments to address theproblem of detecting small targets within sea clutter. We first extract threesimple yet practically discriminative features from the returned signals in thetime and frequency domains and then fuse them into a 3-D feature space. Basedon the constructed space, we then adopt and elegantly modify the support vectormachine (SVM) to design a learning-based detector that enfolds the false alarmrate (FAR). Most importantly, our proposed detector can flexibly control theFAR by simply adjusting two introduced parameters, which facilitates toregulate detector's sensitivity to the outliers incurred by the sea spikes andto fairly evaluate the performance of different detection algorithms.Experimental results demonstrate that our proposed detector significantlyimproves the detection probability over several existing classical detectors inboth low signal to clutter ratio (SCR) (up to 58%) and low FAR (up to 40%)cases.

SVM-Based Sea-Surface Small Target Detection: A False-Alarm-Rate-Controllable Approach