Change Detection of Foliage-concealed Targets Based on Deep Neural Network in Low Frequency Ultra-wideband SAR Images

The low frequency ultra-wideband synthetic aperture radar (LF UWB BSAR) has a perfect capability of penetrating the vegetation and obtaining the high-resolution SAR image, which can be used to detect the foliage-concealed target under the jungles. However, the LF UWB SAR image usually includes some strong scattering points of the non-targets, such as the thick tree trunks. A change detection method of the foliage-concealed targets based on the deep neural network is presented in this paper for the LF UWB SAR images. Using the method of the data augmentation and random cutting, the LF UWB SAR image dataset is obtained from the CARABAS-II SAR dataset, which contains the foliage-concealed targets with the annotation. Besides, a deep neural network is designed for the prediction and detection of the foliage-concealed target in the LF UWB SAR images. The input is the previously generated LF UWB SAR image dataset, and the output is 1 or 0 which is used as the target judgment. Finally, the deep neural network is trained and tested, and the accuracy of the deep neural network for the change detection of the foliage-concealed targets in the LF UWB SAR images is obtained. Experimental results prove that the presented algorithm cannot only achieve the change detection of the foliage-concealed target in LF UWB SAR images, but also can keep the fast detection of the multi-targets but with a higher precision.