基于超像素與稀疏重構顯著性的極化SAR艦船檢測

羅嘉豪; 殷君君; 楊健

doi:10.13374/j.issn2095-9389.2022.12.28.002

基于超像素與稀疏重構顯著性的極化SAR艦船檢測

doi: 10.13374/j.issn2095-9389.2022.12.28.002

1.
北京科技大學計算機與通信工程學院，北京 100083
2.
清華大學電子工程系，北京 100084

基金項目: 國家自然科學基金資助項目（62222102, 62171023, U20B2062）

詳細信息

通訊作者:
E-mail: junjun_yin@ustb.edu.cn

中圖分類號: TN958
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- 文章訪問數: 237
- HTML全文瀏覽量: 49
- PDF下載量: 28
- 被引次數: 0
出版歷程
- 收稿日期: 2022-12-28
- 網絡出版日期: 2023-03-21
- 刊出日期: 2023-10-25

Polarimetric SAR ship detection based on superpixel and sparse reconstruction saliency

1.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

More Information

Corresponding author: E-mail: junjun_yin@ustb.edu.cn

摘要

摘要: 極化SAR艦船檢測是極化SAR系統的重要應用之一，現有的極化SAR艦船檢測方法在具有強背景雜波的條件下容易將強雜波誤檢為目標，造成虛警；在多尺度艦船檢測情況下小尺寸的艦船容易淹沒在背景雜波中，造成小尺寸目標的漏檢。針對上述問題，本文提出一種基于超像素與稀疏重構顯著性的極化SAR艦船檢測方法。該方法首先用超像素分割方法將大幅的極化SAR場景圖像分割，在超像素級別上使用稀疏重構顯著性方法保留含有艦船目標的超像素，再在這些保留下來的超像素中逐像素使用稀疏重構顯著性檢測方法，得到最終的艦船檢測結果。本文選取強雜波場景和多尺度艦船檢測場景的兩個場景的ALOS-2衛星極化SAR數據進行對比實驗，實驗結果表明，本文方法在強雜波場景下品質因數達到94.87%，在多尺度艦船檢測場景下品質因數達到94.05%。
- 極化SAR /
- 艦船檢測 /
- 稀疏重構 /
- 超像素分割 /
- 顯著性檢測
Abstract: Polarimetric SAR ship detection is an important application of the polarimetric SAR system. Existing polarimetric SAR ship detection methods are plagued by erroneous detection of strong clutter and missed detection of small targets in multiscale situations. Particularly, the existing methods easily detect strong clutter as the target under strong background clutter, resulting in false alarms; in the case of multiscale ship detection, small ships are easily submerged in background clutter, resulting in missed detection of small targets. To solve these problems, this paper proposes a polarimetric SAR ship detection method based on superpixels and sparse reconstruction saliency. This method has two stages. In the first stage, the large polarimetric SAR ship detection scene image is segmented using the superpixel segmentation method to obtain a superpixel image. With the superpixel as the basic unit, a saliency detection method based on sparse reconstruction is used to obtain the saliency value of each superpixel in the image. Then, the superpixels that may contain ship targets are retained using the sea surface ship density defined in this paper. Accordingly, in the first stage, the superpixel regions that may contain ship targets are obtained through superpixel segmentation and sparse reconstruction saliency detection. Next, in the second stage, a saliency detection method based on sparse reconstruction is used to obtain the saliency value of each pixel in these reserved superpixel regions. Finally, the global threshold segmentation method is used for the pixels in these regions to obtain the final detection results of ship targets. In this paper, two polarimetric SAR images of the ALOS-2 satellite with different scenes were selected for an experiment. One image contains strong clutter on the sea surface; the other contains ships of different sizes and many small ships. The experimental results show that the proposed method can well determine the superpixel regions that may contain ship targets in the first stage and successfully obtain the ship detection results in the second stage. In addition, in both scenarios, the classic constant false alarm rate (CFAR) methods and a saliency detection method are used for comparison with the proposed method. The experimental results show that the proposed method produces almost no false alarms because it is insensitive to strong clutter; moreover, this method rarely misses small ship targets in the multiscale ship detection scene. The figure of merit of the proposed method reaches 94.87% in the strong clutter scene and 94.05% in the multiscale ship detection scene.
- polarimetric SAR /
- ship detection /
- sparse reconstruction /
- superpixel segmentation /
- saliency detection

HTML全文

圖 1 基于稀疏重構的顯著性目標檢測流程圖

Figure 1. Flowchart of object detection based on sparse reconstruction saliency

下載: 全尺寸圖片幻燈片

圖 2 基于超像素與稀疏重構顯著性的極化SAR艦船檢測流程圖

Figure 2. Flowchart of polarimetric SAR ship detection based on superpixel and sparse reconstruction saliency

下載: 全尺寸圖片幻燈片

圖 3 SLIC超像素分割效果圖. (a) SLIC; (b) 基于修正Wishart分布的SLIC

Figure 3. SLIC superpixel segmentation results: (a) SLIC; (b) SLIC based on the revised Wishart distribution

下載: 全尺寸圖片幻燈片

圖 4 極化SAR數據Span灰度圖

Figure 4. Span grayscale image of polarimetric SAR data

下載: 全尺寸圖片幻燈片

圖 5 雜波模板的Span灰度圖

Figure 5. Span grayscale image of the clutter template

下載: 全尺寸圖片幻燈片

圖 6 超像素檢測結果圖. (a) P=0.3; (b) P=0.1

Figure 6. Superpixel detection result: (a) P = 0.3; (b) P = 0.1

下載: 全尺寸圖片幻燈片

圖 7 最終的艦船檢測結果

Figure 7. Final ship detection results

下載: 全尺寸圖片幻燈片

圖 8 場景一和場景二的Pauli為彩色圖與真值圖. (a)場景一的Pauli偽彩色圖; (b)場景一的真值圖; (c)場景二的Pauli偽彩色圖; (d)場景二的真值圖

Figure 8. Pauli pseudo-color maps and truth maps of scene 1 and scene 2: (a) Pauli pseudo-color map of scene 1; (b) truth map for scene 1; (c) Pauli pseudo-color map of scene 2; (d) truth map for scene 2

下載: 全尺寸圖片幻燈片

圖 9 場景一的艦船檢測結果. (a) CA-CFAR; (b) OS-CFAR; (c) 顯著性方法; (d) 本文方法; (e) 場景一的真值圖

Figure 9. Ship detection results in scene 1: (a) CA-CFAR; (b) OS-CFAR; (c) saliency method; (d) our method; (e) truth map for scene 1

下載: 全尺寸圖片幻燈片

圖 10 場景二的艦船檢測結果. (a) CA-CFAR; (b) OS-CFAR; (c) 顯著性方法; (d) 本文方法; (e) 場景二的真值圖

Figure 10. Ship detection results in scene 2: (a) CA-CFAR; (b) OS-CFAR; (c) saliency method; (d) our method; (e) truth map for scene 2

下載: 全尺寸圖片幻燈片

圖 11 場景一中的特殊漏檢結果

Figure 11. Special missed detection result in scene 1

下載: 全尺寸圖片幻燈片

圖 12 場景二中的特殊漏檢結果

Figure 12. Special missed detection result in scene 2

下載: 全尺寸圖片幻燈片

表 1 場景一極化SAR艦船定量檢測結果

Table 1. Polarization SAR ship quantitative detection results of scene 1

Method Nc N_FA N_M FoM/%

CA-CFAR 37 3 2 88.10
OS-CFAR 36 5 3 81.82
Saliency method 35 0 4 89.74
Our method 37 0 2 94.87

下載: 導出CSV

表 2 場景二極化SAR艦船定量檢測結果

Table 2. Polarization SAR ship quantitative detection results of scene 2

Method Nc N_FA N_M FoM/%

CA-CFAR 75 0 7 91.46
OS-CFAR 77 6 5 87.50
Saliency method 65 0 17 79.27
Our method 79 2 3 94.05

下載: 導出CSV

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