区域筛选与多级特征判别相结合的PolSAR图像飞机目标检测
Aircraft target detection of PolSAR image combined with regional screening and multi-feature discriminant
- 2019年24卷第7期 页码:1197-1206
收稿:2018-08-29,
修回:2019-1-7,
纸质出版:2019-07-16
DOI: 10.11834/jig.180503
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收稿:2018-08-29,
修回:2019-1-7,
纸质出版:2019-07-16
移动端阅览
目的
2
针对全极化、复杂场景下飞机目标检测问题,提出了区域筛选与多级特征判别相结合的PolSAR飞机目标检测方法。
方法
2
首先对原始PolSAR图像进行滤波及去取向预处理,消除相干斑和随机取向对检测效果的影响;其次对图像进行基于功率值的区域分割,提取感兴趣区域;然后对感兴趣区域进行区域筛选,提取疑似飞机目标;最后以功率交叉熵、背景匀质性、功率差异度为特征对疑似飞机目标进行筛选,得到最终的检测结果。
结果
2
利用美国NASA实验室的AIRSAR和UVASAR系统采集的Half-Moon-Bay、Kahului及Kona地区的实测数据进行实验,并与其他方法进行了对比。在实验1中,本文方法和对比方法均能准确检测出场景中存在的2架飞机目标,本文方法产生了7个虚警,对比方法产生了22个虚警;在实验2中,本文方法和对比方法都检测出了4架飞机目标,本文方法产生了4个虚警,对比方法产生了17个虚警;在实验3中,本文方法检测出了15架飞机中的13架,产生了6个虚警,对比方法检测出了6个待测目标,产生了17个虚警。
结论
2
本文方法在提取出疑似飞机目标的前提下,利用多种特征对疑似飞机目标进行筛选,不需要提取出机场跑道和停机坪区域,避免了由于跑道和停机坪区域提取不完整导致的检测不准确的问题,相比于对比方法,本文方法在降低虚警和漏警的同时,提高了运算效率。
Objective
2
Few studies on the target detection of PolSAR images that can be referred worldwide are currently available. In the full polarization SAR image of complex scenes
difficulties in aircraft target detection exhibit the following aspects: On the one hand
the background part includes not only the airport but also several areas
such as city
forest
mountain
ocean
and road
due to the different statistical characteristics of each area. Hence
fitting all the statistical characteristics of the background with a statistical feature is impossible. On the other hand
the polarization characteristics of the vehicle
ship
and some small buildings are particularly similar to those of the aircraft target in the SAR image. Therefore
aircraft targets are difficult to distinguish from other targets with one feature. Moreover
the shape of the aircraft target cannot be presented in the full polarization PolSAR image due to the resolution of the PolSAR data obtained by the imaging system
which can only be expressed by some pixel features. The existence of these problems complicates the detection of aircraft targets in PolSAR images. Result shows that the aircraft target exhibits several characteristics in the PolSAR image: 1) the scattering power of the aircraft target is generally higher than that of the surrounding background; 2) the aircraft target is often parked in fixed areas
such as airports and aprons
and the regions are characterized by homogeneity; and 3) the aircraft target is presented in the form of pixel blocks in the PolSAR image.
Method
2
An aircraft target detection algorithm combined with regional screening and multi-feature discriminant is proposed to solve the abovementioned problems and combine prior data. First
image preprocessing is performed to minimize the effect on the original PolSAR image due to the speckle and random orientation from target reflection. Second
the regions of interest of the runway
tarmac
and regions whose scattering properties are similar are extracted in accordance with the image power value. Then
suspected aircrafts are extracted by the area of connected domain. Finally
prior knowledge indicated that the power of the aircraft target is relatively large
the scattering power of the airport area is comparatively small
the aircraft target tail and wing roots demonstrate dihedral structural features
and the aircraft targets often appear in the airport or apron area. Thus
the suspected aircrafts are screened in terms of different characteristics
such as power cross entropy
background homogeneity
and power difference.
Result
2
Experiments were performed with the polarimetric synthetic aperture radar data from Half-Moon-Bay and Kahului Kona acquired by AIRSAR and UVASAR systems from NASA Laboratories in the United States. Few documents on the target detection of PolSAR image aircraft are available. Thus
the experiment was only compared with one method. The detection result of Half-Moon-Bay shows that both methods can accurately detect the aircraft targets. However
the proposed method produces seven false alarms. By contrast
the comparison method produces 22 false alarms. The Kahului result shows that the proposed and comparison methods can detect four aircraft targets. Nonetheless
the proposed method produces four false alarms. Contrarily
the comparison method produces 17 false alarms. The Kona result shows that the proposed method detects 13 out of 15 aircrafts and produces six false alarms. By contrast
the comparison method detects six out of 15 targets and produces 17 false alarms. The time spent in the experiment implies that the algorithm exhibits high computational efficiency.
Conclusion
2
The method eliminates the false targets by fusing different features to extract the suspected aircraft target and then obtain the final test results. The algorithm does not need to extract the airport runway and apron area
thereby avoiding inaccurate detection caused by the incomplete extraction of the apron area. The final test results produce few leak alarms
and some false alarms were generated
but the proposed method simultaneously produces fewer false and leak alarms than the comparison method. This method presents great improvement in operational efficiency because it only needs to traverse the extracted suspected target and not all the pixel points. However
the proposed algorithm still needs improvement. For example
the algorithm must be improved in terms of controlling false alarms. The false alarms generated in the algorithm are small buildings
vehicles
and ships because their characteristics in the PolSAR image are similar to the aircraft target. The parameter selection remains unable to achieve complete self-adaptation because the background area contains not only the airport but also other areas
such as urban
forests
mountains
and oceans. The background's statistical properties cannot be fitted with distribution. In addition
when the two targets are close to each other
the target background area obtained by morphological expansion may include the target to be detected
which may have a certain influence on the result. Thus
these problems must be solved in future works.
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