复杂场景下遥感船舶的检测与分割定位

遥感图像船舶识别是目标识别的一个重要领域,在海防和救援方面具有重大应用价值.但遥感图像中的船舶普遍存在云雾遮挡、陆地背景干扰和体积小等因素所造成的识别难的问题.为了能准确识别复杂场景下船舶目标,在网络的特征提取部分加入了视觉注意机制,增强网络提取船舶特征信息的能力;并采用多级特征提取和去量化操作的学习方法来解决船舶体积小的问题;采用难样本重学习的学习策略来弱化云雾遮挡和陆地背景的干扰.通过上述方法,船舶识别的综合准确率达到了92.56%,召回率达到了89.26%,与相同实验环境(PyTorch)下其他常见目标检测算法相比,精确率和召回率都有明显提升.实验结果表明,文中方法在一定程度上解决了复杂场景下船舶分割和识别难的问题.实验中所使用代码和部分结果详见https://github.com/curioyang/First_paper.

Detection and Segmentation and Positioning of Remote Sensing Ships in Complex Scenes

Remote sensing image ship recognition is an important field of object identification, which has great application value in sea defense and rescue. However, the ship in remote sensing images is characterized by difficult identification caused by cloud and fog, land background interference and small size. To accurately identify ship objects in complex scenes, a visual attention mechanism is added to the feature extraction part of the network, which enhances the ability of the network to extract ship characteristic information. The learning method of multi-level feature extraction and de-quantitative operation is used to solve the problem of small ship size, and the learning strategy of hard example re-learning is used to weaken the interference of cloud mask and land background. Through the above methods, the comprehensive accuracy rate of ship identification reached 92.56%, the recall rate reached 89.26%, compared with other common object detection algorithms under the same experimental environment(PyTorch), the accuracy rate and recall rate have been significantly improved. Experimental results show that, to some extent, the proposed method solves the problem of ship segmentation and identification difficult in complex scenes. The code used in the experiment and some of the results are found in: https://github.com/curioyang/First_paper.