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

Shiyuan Chen,Zhiliang Li,Mao Yuxing,Xiaojiang Li,Shaoquan Chi

doi:10.1109/access.2020.3022642

Shiyuan Chen, Zhiliang Li + Show 3 more

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https://doi.org/10.1109/access.2020.3022642

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 8	License type: CC BY 4.0

Affiliation: Space Engineering University

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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.

Highlights

With the rapid development of Synthetic aperture radar (SAR) imaging technology, SAR images are widely used in military and civil fields [1]–[3]
According to the above analysis, in this article, we propose a new discrimination method based on the bag of words (BOW) model to deal with SAR ship target discrimination in complex scenes In the local feature extraction stage, SAR-scale invariant feature transform (SIFT) descriptors and gray-level co-occurrence matrix (GLCM) descriptors are extracted to describe the difference of target and clutter
EXPERIMENTAL RESULTS AND ANALYSIS To better investigate the discrimination performance of the proposed method based on MF-spatial pyramid matching (SPM)-BOW model and better understand the effects of the multiple features and the SPM technique, we conducted a series of experiments with different types of SAR images

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Introduction

With the rapid development of Synthetic aperture radar (SAR) imaging technology, SAR images are widely used in military and civil fields [1]–[3]. One of the most important applications is SAR ship target detection and recognition, which has attracted much more attention during the past decades [4], [5], which has attracted more and more attention. The ship target detection selects the candidate ship target chips from the whole image. Due to the complex conditions of sea surface and serious interference of clutter, there exist many false alarm chips in the selected target chips, namely clutter false alarms, land and island false alarms. The false alarms interfere with the subsequent ship target recognition and. Recently many algorithms and techniques have been utilized to high-resolution SAR ship target discrimination to eliminate the false alarms

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