【摘要】：目的為了解決復雜背景干擾下基于線性濾波異常檢測算法無法有效區(qū)分復雜背景特征與異常目標特征,導致檢測結(jié)果虛警率偏高的問題,提出一種面向復雜背景的遙感異常小目標仿生非線性濾波檢測算法。方法受生物視覺系統(tǒng)利用不同屬性信息挖掘高維特征機理的啟發(fā),該算法通過相關型非線性濾波器綜合多波段光譜數(shù)據(jù)提取高維光譜變化特征作為異常目標檢測檢測依據(jù),彌補線性濾波抗噪性能差,難于區(qū)分復雜背景特征與目標特征的缺點。結(jié)果仿真實驗結(jié)果驗證該算法在仿真數(shù)據(jù)及真實遙感數(shù)據(jù)的異常檢測效果上有較大改善,在實現(xiàn)快速異常檢測的同時提高了檢測命中率。結(jié)論本文方法不涉及背景建模,計算復雜度低,具有較好的實時性與普適性。特別是對復雜背景下的小尺寸異常目標具有較好的檢測效果。
[Abstract]:Aim in order to solve the problem that the anomaly detection algorithm based on linear filtering can not effectively distinguish the complex background features from the abnormal target features under complex background interference, and lead to the high false alarm rate of the detection results, a bionic nonlinear filtering algorithm for remote sensing abnormal small targets for complex background is proposed. Methods inspired by the mechanism of mining high-dimensional features by using different attribute information in biological vision system, the algorithm uses correlation nonlinear filter to synthesize multi-band spectral data to extract high-dimensional spectral variation features as the basis of abnormal target detection, which makes up for the poor anti-noise performance of linear filtering and difficult to distinguish complex background features from target features. Results the simulation results show that the algorithm improves the anomaly detection effect of simulation data and real remote sensing data, and improves the hit rate of detection while realizing fast anomaly detection. Conclusion the proposed method does not involve background modeling, has low computational complexity, and has good real-time and universality. Especially, it has a good detection effect for small size abnormal targets in complex background.
【作者單位】： 河海大學物聯(lián)網(wǎng)工程學院;
【基金】：國家自然科學基金項目(41301448,61573128,61273170)~~
【分類號】：TP751

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本文編號：2500395