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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Ship detection for SAR imagery plays a crucial rule in the field of remote-sensing image processing. Superpixel methods have attracted enormous interest in the recent years, resulting in superpixel-based CFAR (Constant False Alarm Rate) ship detection methods for SAR images become a hot research issue. However, existing methods take superpixel generation as a preprocessing, and CFAR detection is essentially carried out at the pixel level, and the influence of speckle noise has not been fundamentally overcome. This paper innovatively proposed a real superpixel-level CFAR detection method, which takes superpixels instead of pixels as the minimum processing primitives on the basis of reliable superpixel segmentation. Firstly, in order to obtain the background superpixel region of each under test superpixel more accurately and efficiently, we designed a ring topology, which was specially developed for non-Euclidean structure data like superpixels. Secondly, the Johnsonsu function is used to fit the superpixel clutters from the background ring, and the detection threshold was obtained by a given false alarm rate. Finally, the candidate targets were further optimized to carry out accurate detection results. Experimental results based on multiple sets of real SAR images shown that our method has significant advantages in terms of detection rate and false alarm rate compared to the traditional K-CFAR and the existing superpixel-based CFAR methods.

Ship detection based on a superpixel-level CFAR detector for SAR imagery