关键词: 遥感图像, Mask R-CNN, 舰船检测, 旋转不变, Fisher判别

Abstract:

The uncertainty of ship orientation in remote sensing images, the diversity of ship types and the similarities with other sea and port objects have caused the performance of ship detection to deteriorate seriously. Aiming at this problem, a simple and effective method is used to train the Mask R-CNN ship detection model with rotation invariance and Fisher discriminant. By optimizing the model’s objective function to improve ship detection performance, the original detection is maintained. Based on the invariant model structure, two regularizers are introduced. The first regularizer enhances the feature association before and after training sample rotation. The second regularizer limits the convolutional neural network to have small intra-class divergence and large inter-class divergence. Experimental results on Kaggle remote sensing image ship detection dataset verify the proposed method, which improves performance of detecting ship targets in remote sensing images.

Key words: remote sensing image, Mask R-CNN, ship inspection, rotation unchanged, Fisher discrimination