Land cover semantic segmentation of Port Area with High Resolution SAR Images Based on SegNet

The SAR image port region semantic segmentation algorithm combined with the deep learning mechanism is of great significance to the port intelligence and information construction. However, there are few researches on the pixel-level semantic segmentation of port area with SAR images. The port spatial pattern monitoring based on remote sensing images still has problems such as complicated monitoring procedures, low robustness, and insufficient datasets. To take advantage of SAR images and explore the potential of convolutional neural networks in the SAR port area images, a dataset containing eight land types of port was established, and a pixel-level segmentation framework for port areas based on SegNet was proposed in this paper. The pixel-level classification of high-resolution SAR port area images based on small datasets was realized. The comparison experiments with SVM and random forest algorithm proved that the algorithm proposed in this paper can achieved an overall accuracy of 91.6% on a SAR image of port area, and better maintain the edge characteristics of different land types.