SAR target image edge detection based on CNN

Abstract

Aiming at the problems that the classical edge detection method is easily affected by noise and has low detection accuracy when applied to SAR target images, this paper studies the detection performance of the classical edge detection method Canny, CNN-based edge detection methods Holistically Nested Edge Detection (HED) and Richer Convolutional Features (RCF) when applied to SAR target images for the first time. The detection performance is evaluated using the MSTAR dataset, and the detection results of each method are compared based on the common evaluation indicators of image edge detection: F-measure, PR curve, and FPS. Canny's F-measure (ODS) is 0.611 and FPS is 43. The F-measure (ODS) of HED is 0.758 and the FPS is 18. The F-measure (ODS) of RCF is 0.729 and the FPS is 24. The F-measure (ODS) of RCF-MS is 0.753 and the FPS is 6. On the MSTAR dataset, the F-measure of HED is the best, which is 24.06% higher than Canny. RCF and RCF-MS also performed well, which were 19.31% and 23.24% higher than Canny respectively. The edge detection method based on CNN has higher F-measure, is less affected by noise, and has less loss of edge details. When applied to SAR images affected by speckle noise, the performance is much better than Canny, but there is still a shortage of slightly worse computing speed.