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.