Aircraft detection in synthetic aperture radar (SAR) images plays an essential role in both civil and military fields. However, due to the special imaging mechanism of SAR images, the aircraft annotating process is easily affected by interferences and noises in the background, leading to a high labeling cost. As most object detection networks are trained in a supervised manner, a serious problem of applying them to SAR aircraft detection tasks is the insufficient training data. To address this problem, we propose an unsupervised domain adaption method for the training of SAR aircraft detectors. First, we propose to transfer knowledge from optical aerial images in which aircraft annotations are easier to obtain. By adopting an image-level domain adaption, the target information in optical images can be utilized for the training of SAR aircraft detectors. Then, CycleGAN is adopted to overcome the discrepancy between optical and SAR domains by image-style translation. To evaluate the effectiveness of the proposed method, we build up an optical-to-SAR aircraft detection dataset (O2SADD) based on existing public datasets. Experiments on O2SADD indicate that the proposed method can significantly improve the performance of SAR aircraft detectors without manually annotating aircraft in SAR images.
Synthetic aperture radar (SAR) image registration is significant for mapping, measurement, navigation, and so on. However, it is problematic for the SAR image alignment because the multiplicative speckle noise in the image and the coherent imagery mechanism of the SAR. Therefore, an advanced feature-based image registration method for the SAR image is proposed in this paper. Firstly, the speckle noise is filtered in natural SAR image based on the weighted nuclear norm minimization, which makes the amount of the false feature point reduce. Secondly, with the defined gradient for the SAR image, the improved SIFT method is employed to extract the feature point and generate the descriptor. The experimental results show that, compared to other methods, the proposed method improves both the accuracy of alignment and utilization of feature point significantly.
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