一種VideoSAR動目標陰影檢測方法

張營; 朱岱寅; 俞翔; 毛新華

doi:10.11999/JEIT161394

一種VideoSAR動目標陰影檢測方法

doi: 10.11999/JEIT161394

1.
(南京航空航天大學電子信息工程學院南京 211106) ②(南京工程學院計算機工程學院南京 211167)

基金項目:

國家自然科學基金(61671240)，江蘇省自然科學基金青年基金(BK20150730)，中央高?；究蒲袠I(yè)務(wù)費(NZ2016105)，南京航空航天大學研究生創(chuàng)新基地(實驗室)開放基金資助項目(kfjj20170401)

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- 文章訪問數(shù): 2028
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-12-29
- 修回日期: 2017-04-24
- 刊出日期: 2017-09-19

Approach to Moving Targets Shadow Detection for VideoSAR

1.
(College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Funds:

The National Natural Science Foundation of China (61671240), The Natural Science Foundation of Jiangsu Province for Youths (BK20150730), The Fundamental Research Funds for the Central Universities (NZ2016105), The Foundation of Graduate Innovation Center in NUAA (kfjj20170401)

摘要

摘要: 在高幀率的視頻合成孔徑雷達(VideoSAR)成像模式獲得的圖像序列中，多普勒頻移使運動目標在實際位置留下陰影，且相鄰幀圖像具有很強相關(guān)性。該文針對上述現(xiàn)象提出一種VideoSAR圖像中動目標陰影檢測的方法。首先，對每幀圖像通過結(jié)合尺度不變特征變換(SIFT)和隨機抽樣一致性(RANSAC)算法實現(xiàn)配準并進行背景補償，再采用CattePM模型抑制相干斑噪聲。然后通過Tsallis灰度熵的最大化閾值分割方法自動分離目標和背景，獲得二值圖像。最后，對相鄰多幀圖像背景建模并差分，再結(jié)合三幀間差分法提取動目標陰影，結(jié)果標記至原幀圖像相應(yīng)位置。基于美國Sandia實驗室公布的VideoSAR成像片段，實現(xiàn)了多個移動車輛的檢測，驗證了所提算法的有效性。
- 視頻合成孔徑雷達 /
- 動目標陰影檢測 /
- 圖像配準 /
- 閾值分割 /
- 幀間差分
Abstract: In the image sequence obtained by the high frame rate Video Synthetic Aperture Radar (VideoSAR) mode, the Doppler shift results in some shadows of the moving targets in their actual position, and a strong correlation exists between adjacent frames. Based on the above rationale, this paper proposes an approach to detecting moving targets shadow in VideoSAR imagery. First, the Scale-Invariant Feature Transform (SIFT) with RANdom SAmple Consensus (RANSAC) registration algorithm is used to compensate for the change of background of each frame, and the CattePM model is employed to suppress the speckle noise effectively. Then, in order to separate the targets and the background and generate binary images automatically, a threshold segmentation algorithm, called maximizing the Tsallis entropy, is applied. Finally, shadow detection is accomplished by the background difference with three frame difference method, and the detection results are marked on the corresponding position in the original frame. Experimental results utilizing the VideoSAR imaging fragment published by Sandia National Laboratories show that multiple moving vehicles are detected effectively, hence the validity of the approach is demonstrated.
- Video Synthetic Aperture Radar (VideoSAR) /
- Moving target shadow detection /
- Image registration /
- Threshold segmentation /
- Frame difference

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參考文獻(20)

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