Foliage-Concealed Target Change Detection Scheme Based on Convolutional Neural Network in Low-Frequency Ultrawideband SAR Images

The low-frequency ultrawideband synthetic aperture radar (UWB SAR) has the ability of the foliage-penetrating and high-resolution imaging, which can detect the foliage-concealed target. However, due to low-frequency UWB SAR characteristics and forest detection environments, there are usually some nontarget strong scattering points in the low-frequency UWB SAR images, which may increase the difficulty of the foliage-concealed target change detection. To solve the problem of the weak antijamming ability of the foliage-concealed target change detection, a foliage-concealed target change detection scheme based on the convolutional neural network in the low-frequency UWB SAR images is proposed, which combines the traditional image difference method and deep-learning method. First, a relatively low inspection threshold is set for the target change detection based on the image difference method, which can obtain a lot of the position information of the detection point. Moreover, for the target characteristics in the foliage-concealed scenarios, the corresponding data extraction and enhancement techniques are used to effectively extract the detection point samples from the detection image and reference image, which can prevent the overfitting of the model training caused by the sample scarcity. Finally, the samples of the detection points are input to the target classification network with the double input and single output for the classification training and testing. The experimental results tested on the CARABAS-II SAR dataset demonstrate the correctness and effectiveness of the proposed scheme, which has the better change detection performance and anti-interference capability.