| 孙丹,王莉莉.基于空-频域映射与虚警抑制的弱小目标检测算法[J].电子测量与仪器学报,2019,33(1):31-39 |
| 基于空-频域映射与虚警抑制的弱小目标检测算法 |
| Dim target detection algorithm based on spatial frequency domain mapping and false alarm suppression |
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| DOI： |
| 中文关键词: 红外弱小目标检测 空域映射 频域映射 方向最大中值滤波器 云区域识别 改进的管道滤波 |
| 英文关键词:infrared dim and small target detection spatial domain map frequency domain map directional maximum median filter cloud area identification improved pipeline filtering |
| 基金项目:四川省教育厅自然科学项目(18ZB0011)、江苏省高等学校自然科学研究项目(18KJD520011)、阿坝师范学院校级规划项目（ASB17 05）资助 |
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| 中文摘要: |
| 为了有效控制红外弱小目标检测过程中的虚警率，提高复杂云背景下的目标检测准确度，提出了基于空域 频域映射与虚警抑制的弱小目标检测算法。根据红外中心像素不同方向上的强度值，构建了方向最大中值滤波器，有效消除噪声；并利用中心像素与其邻域像素的强度差异，形成背景抑制滤波，充分增强弱小目标；考虑云区域的特有属性，联合非线性滤波，定义了云区域识别机制，提取空域映射；引入Butterworth差异低通滤波器，对去噪图像中的显著目标完成初步识别；基于其幅度信息，进行显著目标的精细检测；再利用细显著性检测结果，计算阈值，利用二值分割方法来获取去噪红外图像的频域映射；联合空域映射与频域映射，提取红外图像中的候选目标；根据真实动目标与虚警之间的运动特征差异，利用多尺度改进的管道滤波来抑制虚警，准确识别出真实目标。实验数据表明，相对于已有的弱小目标识别方案，所提方案能够准确地识别出真实目标，拥有更好的ROC特性曲线。 |
| 英文摘要: |
| In order to effectively control the false alarm rate in the process of infrared dim small target detection and improve the accuracy of target detection in complex cloud background, A small target detection algorithm based on spatial frequency domain mapping and false alarm suppression was proposed in this paper. A directional maximum median filter is constructed according to the intensity values in different directions of the infrared central pixel to effectively eliminate noise. And a background suppression filter is formed by using the intensity difference between the center pixel and its neighboring pixels to fully enhance the dim target. Considering the unique attributes of cloud region, a cloud region recognition mechanism was defined to extract spatial mapping by combining with nonlinear filtering. The Butterworth differential low pass filter is introduced to complete the coarse saliency detection of the denoised image. And the fine saliency detection was completed according to the amplitude information of the coarse saliency detection result. Then, the threshold was calculated by using the coarse and fine saliency detection results, so that the frequency domain mapping of denoised infrared image was obtained by using the adaptive binary segmentation method. The candidate targets in infrared images were extracted by jointing spatial map and frequency domain map. According to the difference of motion characteristics between real moving target and false alarm, these false alarms in candidate targets were suppressed based on improved pipeline filtering to accurately detect the real infrared dim targets. Test results show that this algorithm can accurately identify the real target, which has a better ROC characteristic curve compared with current infrared dim and small target detection technology |
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