基于卷积神经网络的遥感图像舰船目标检测
Ship object detection in remote sensing images using convolutional neural networks

摘要 针对遥感图像背景复杂、受环境因素影响大的问题，提出一种将卷积神经网络（CNN）与支持向量机（SVM）相结合的舰船目标检测方法，利用卷积神经网络可自主提取图像特征并进行学习的优点，避免了复杂的特征选择和提取过程，在复杂海况背景图像的处理中体现出较优的性能；同时，由于军舰样本获取难度大，应用迁移学习的概念，利用大量民船样本辅助军舰目标的检测，取得较好的效果。通过参数调整与实验验证，此方法在自行建立的测试集上检测率达到90.59%，对光照、环境等外界因素具有一定程度的鲁棒性。
Abstract：Object detection in remote sensing images is mostly suffered from complex background and multiple interferences of environment. In this paper, a new method of ship detection is proposed, which combines convolutional neural networks (CNN) and support vector machine (SVM) to complete the ship detection task. Convolutional layers were adopted for feature extraction, taking advantages of independent feature extraction of CNNs and avoiding the process of complicated feature selection and extraction, which leads to better detection performance in complex background images. Meanwhile, since the samples of warship are difficult to acquire, samples of civil ship were employed as assistant samples for warship detection based on transfer learning theory. And this transfer learning method is proved to be effective by the experimental results, which performs better than the model trained only with warship samples. According to the parameter tuning and experimental validation, this method achieves a precision of 90.59% on testing dataset established by ourselves. In conclusion, this method possesses feasibility and robustness under different conditions of illumination and environment.