Aircraft Target Detection from Spaceborne SAR Image

Target detection is an important application in remote sensing. In this paper, an end-to-end aircraft detection algorithm is proposed for large scene spaceborne synthetic aperture radar (SAR) imagery. Due to the diversity and variability of scattering mechanism, representative features including edge information and depth characteristics are utilized in the algorithm. Firstly, the accurate airport mask is extracted via Otsu algorithm and adaptive identification operator (AIO) with airport morphological features. Secondly, edge-detection based on Canny operator and k-means clustering algorithm are adopted to generate candidate areas. Finally, aircraft targets are discriminated from candidate areas via ResNet-based convolutional neural network (CNN). Experiments are conducted on collected spaceborne SAR imagery, and the results indicate that the proposed algorithm can extract airport area precisely and detect aircraft accurately with low false alarm.