Aircraft Detection in Sar Images Using Saliency Based Location Regression Network

In this paper, a novel framework for aircraft detection in high resolution apron area in Synthetic Aperture Radar (SAR) images is proposed, which combines the strength of location regression based convolutional neural network (CNN) framework and the salient features of target in SAR images. Specifically, a Constant False Alarm Rate (CFAR) based target pre-locating algorithm is introduced, which can match the scale of target in SAR images more accurate compared to the existing region proposal method. In addition, in order to eliminate the fact of overfitting, we explore several strategies for SAR data augmentation, including translation, adding noise and rotation within a small range. Experiments are conducted on the data set acquired by the TerraSAR-X satellite in a resolution of 3.0 meters. The results show that the proposed detection framework could effectively obtain a more accurate detection result.

[1]  Yihua Tan,et al.  Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map , 2015, Sensors.

[2]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Lei Liu,et al.  Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode , 2017, Sensors.

[4]  Xinwei Zheng,et al.  Efficient Saliency-Based Object Detection in Remote Sensing Images Using Deep Belief Networks , 2016, IEEE Geoscience and Remote Sensing Letters.

[5]  Lance Kaplan,et al.  Improved SAR target detection via extended fractal features , 2001 .

[6]  Robert M. Gagliardi,et al.  Adaptive multiple-band CFAR (Constant-False-Alarm-Rate) detection of an optical pattern with unknown spectral distribution , 1989 .

[7]  C. Lawrence Zitnick,et al.  Edge Boxes: Locating Object Proposals from Edges , 2014, ECCV.

[8]  Clark F. Olson,et al.  Automatic target recognition by matching oriented edge pixels , 1997, IEEE Trans. Image Process..

[9]  Haipeng Wang,et al.  Application of deep-learning algorithms to MSTAR data , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[10]  I. Hajnsek,et al.  A tutorial on synthetic aperture radar , 2013, IEEE Geoscience and Remote Sensing Magazine.

[11]  Rama Chellappa,et al.  Context-aided false alarm reduction for SAR automatic target recognition , 1997, Proceedings of International Conference on Image Processing.

[12]  Leslie M. Collins,et al.  A multiresolution approach to target detection in synthetic aperture radar data , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[13]  Koen E. A. van de Sande,et al.  Selective Search for Object Recognition , 2013, International Journal of Computer Vision.

[14]  Yang Liu,et al.  SAR ship detection using sea-land segmentation-based convolutional neural network , 2017, 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP).

[15]  D. Blacknell,et al.  Contextual information in SAR target detection , 2001 .