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

To eliminate the false alarms in the ship target detection effectively for synthetic aperture radar (SAR) images in complex scenes, this article present a novel ship target discrimination algorithm based on bag of words (BOW) model with multiple features and spatial pyramid matching (SPM), which is named MF-SPM-BOW. The proposed discrimination method mainly contains three stages. First, the SAR scale-invariant feature transform (SAR-SIFT) descriptors and gray-level co-occurrence matrix (GLCM) descriptors are extracted as local features to describe the gradient information and texture information of local regions of an image chip. Then, the SPM technique considering its spatial location information-keeping capability is employed to generate global features with excellent discrimination ability. Finally, the support vector machine (SVM) discriminator based on multiple kernel learning is applied to realize feature fusion in image layer and thus identify targets and clutter. Experimental results show that compared with the traditional discrimination methods and the BOW model discrimination methods, the proposed SAR ship target discrimination algorithm achieves better discrimination performance, which can eliminate most of the false alarms in candidate ship target chips effectively.

Ship Target Discrimination in SAR Images Based on BOW Model With Multiple Features and Spatial Pyramid Matching