一种基于多特征参数融合的弱小目标检测算法（英文）

A Dim Small Target Detection Algorithm Based on Multi-Features Fusion Algorithm

基于特定场景的先验信息,通过分析多个特征参量对弱小目标检测的性能,利用各参量对弱小目标检测的长处,设计了一种基于多特征融合的目标检测算法。以空域匹配模型、区域加权信息熵和频域滤波自适应阈值分割3种方法为单特征量,基于各个特征量对特定应用场景下的大量目标检测的先验结果,利用概率论的知识,构造了有利于提升计算速度和检测概率的两种多特征融合方法。实验表明,该方法能够有效地提高单帧弱小目标的检测性能。

Based on the transcendental information under the specific scene, by analyzing multiple features＇ performance on dim small target detection, this paper designs a dim small target detection algorithm, which combines multiple features＇ Eigen values by taking advantage of the multiple features. Taking the following three features as examples, the Space Gray Model Matching, the Region Gray Weighed Entropy and the Adaptive Thresholding in Frequency Domain, based on the transcendental detection results of multiple features under the specific scene, we propose two multi-Eigen values fusion method by using probability theory. Experiments show that the two methods can both effectively improve the performance of dim small target detection in a single frame.