一種基于變化檢測技術(shù)的SAR圖像艦船目標鑒別方法
doi: 10.11999/JEIT140143 cstr: 32379.14.JEIT140143
基金項目:
國家自然科學基金(61201338)資助課題
A Ship Target Discrimination Method Based on Change Detection in SAR Imagery
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摘要: 該文引入變化檢測思想，利用SAR圖像中海雜波和目標之間的灰度差異，通過對潛在艦船目標切片的目標像素和背景像素進行分離，計算目標像素聚集度(TPAM)特征，實現(xiàn)對高亮像素在圖像切片中聚集程度的定量評估，從而鑒別目標切片中是否包含有艦船目標，有效去除雜波虛警。首先，基于感興趣區(qū)域(ROI)切片中心為目標像素及四周為海雜波的合理假設(shè)，構(gòu)建似然比變化檢測量獲取差異圖像；然后，利用KSW熵閾值選擇方法實現(xiàn)差異圖像中目標像素和海雜波像素的自動分離，生成二值圖像；最后，利用切片中心像素為種子點，對二值圖像進行區(qū)域生長，計算目標像素聚集度特征，并判斷目標切片是否包含艦船目標?；赗ADARSAT-1 SAR實測數(shù)據(jù)的實驗結(jié)果表明，該文方法得到的目標像素聚集度特征計算簡單、穩(wěn)健性好、可區(qū)分度高，具有良好的鑒別性能，能夠去除大部分海雜波干擾產(chǎn)生的虛警，有效地降低目標檢測虛警率。Abstract: In order to reserve ship targets and reduce sea clutters as the false alarms from the SAR Regions Of Interest (ROI) chips, a ship discrimination feature named Target Pixel Aggregative Measure (TPAM) is proposed in this paper. Benefited from the technology of change detection, TPAM using the gray difference in SAR imagery to separate the target pixels and background pixels. Firstly, based on the assumption that the central pixels of a ROI belong to target pixels while the surrounding pixels fall into sea clutters, a change detection measure based on the likelihood ratio is used to generate the residual data. Then the target pixels and background pixels are automatically separated and produce a binary image by the KSW entropy method. Finally, the center of the binary image is used as a seed to implement region growing and TPAM can be obtained to discriminate targets and clutters. Experimental results using RADARSAT-1 SAR data show that the propose discrimination feature is not only simple and robust, but also has a strong differentiate ability, which can eliminate most of false alarms effectively.
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