基于模糊語義的高分辨率SAR圖像汽車檢測算法

黃勇; 劉芳

doi:10.11999/JEIT160650

基于模糊語義的高分辨率SAR圖像汽車檢測算法

doi: 10.11999/JEIT160650 cstr: 32379.14.JEIT160650

黃勇¹,
劉芳¹

基金項目:

國家973計劃項目(2013CB329402)，國家自然科學(xué)基金(61573267)，國家自然科學(xué)基金重大研究計劃(91438201, 91438103)

計量
- 文章訪問數(shù): 1467
- HTML全文瀏覽量: 133
- PDF下載量: 346
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2016-06-21
- 修回日期: 2016-11-07
- 刊出日期: 2017-04-19

Detecting Cars in VHR SAR Images with a Fuzzy Semantic Algorithm

HUANG Yong¹,
LIU Fang¹

Funds:

The National 973 Program of China (2013CB329402), The National Natural Science Foundation of China (61573267), The Major Research Plan of the National Natural Science Foundation of China (91438201, 91438103)

摘要

摘要: 針對高分辨率SAR圖像難于找到精確的背景雜波分布概率模型的問題，該文提出一種不需要背景雜波分布概率模型的高分辨率SAR圖像自動檢測汽車的新方法。該算法首先搜索場景中包含的亮區(qū)域和暗區(qū)域，其次采用模糊隸屬度函數(shù)提取語義特征，篩選可能是汽車強散射區(qū)域的亮區(qū)域和可能是汽車遮擋區(qū)域的暗區(qū)域。再根據(jù)空間語義關(guān)系，對候選汽車強散射區(qū)域與候選汽車遮擋區(qū)域進行匹配，若匹配成功則計算它們源于同一輛汽車的隸屬度。最后閾值選擇高隸屬度的目標(biāo)進行合并輸出。通過對MiniSAR圖像進行汽車檢測實驗，表明該方法在不需要背景雜波分布概率模型的條件下仍然具有較高的檢測率。
- 高分辨率SAR /
- 圖像處理 /
- 目標(biāo)檢測 /
- 圖像語義 /
- 模糊隸屬度
Abstract: It is hard to select a probability distribution model for very high resolution SAR images. This paper presents a novel method for the automatic detecting of cars from VHR SAR image without the probability distribution model. The proposed method starts with searching bright regions and dark regions by the gray feature. Subsequently, the fuzzy membership is employed to extract the semantic features of car from bright regions and dark regions. The potential scattering surface and shadow are matched and calculated with the spatial semantic relationship. Finally, the cars are selected from the matching. The efficiency of the proposed method is demonstrated by experiment which shows it still has high detection rate without the probability distribution model.
- Very High Resolution SAR (VHR SAR) /
- Image processing /
- Object detection /
- Image semantic /
- Fuzzy membership

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參考文獻(22)

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