Abstract: Disclosed are a method and apparatus for SAR image data enhancement, and a storage medium. The method includes: processing an SAR target image by electromagnetic simulation to acquire an SAR electromagnetic simulation image; and processing the SAR electromagnetic simulation image and the SAR target image by a generative adversarial network to obtain a set of virtual samples of the SAR target image.

Abstract: The present disclosure discloses a method for measuring an antenna downtilt based on linear regression fitting, including: performing image instance segmentation on an inputted original antenna image using a deep learning method to obtain a segmented image; performing mask processing on the segmented image; performing mathematically linear modeling and fitting on the segmented image subjected to mask processing; and the performing mathematically linear modeling and fitting on the segmented image subjected to mask processing including: extracting pixel value coordinates of an antenna edge contour from the segmented image subjected to mask processing, and capturing a pixel value of a right-end edge of the antenna; and fitting the pixel value coordinates into a straight line by using a mathematically linear modeling and fitting method to obtain an angle of the antenna downtilt.

Abstract: Disclosed are method and apparatus for SAR image recognition based on multi-scale features and broad learning. A region of interest of an original SAR image is extracted by centroid localization, the image is rotated and added with noise for enhancing the data volume, the image is downsampled, LBP features and PPQ features are extracted, an LBP feature vector XLBP and an LPQ feature vector XLPQ are cascaded to achieve dimension reduction by principal component analysis to obtain a fusion feature data Xm, the fusion feature data Xm is input to a broad learning network for image recognition and a recognition result is output. By fusing the LBP features and the LPQ features, complementary information is fully utilized and redundant information is reduced. The broad learning network is used to improve the training speed and reduce the time cost. As a result, the recognition effect is more stable, robust and reliable.

Abstract: The present disclosure discloses an error modeling method and device for prediction context of reversible image watermarking. A predictor based on omnidirectional context is established; then, the prediction context is self-adaptively error modeled to obtain a self-adaptive error model; and finally, output data from the self-adaptive error model is fed back to the predictor to update and correct the prediction context, so as to correct a prediction value of a current pixel x[i,j]. Since the non-linear correlation between the current pixel and the prediction context thereof, i.e., the non-linear correlation redundancy between pixels can be found by the error modeling of the prediction context of the predictor, the non-linear correlation redundancy between the pixels can be effectively removed. Thus, the embeddable watermarking capacity can be increased.