目前，受限于数据集精细度与网络结构，深度学习技术仍难以应对飞机目标型号识别这类精细化识别任务。本文针对遥感影像中飞机目标型号识别问题，提出一种融合目标检测与关键点检测的飞机型号识别方法。该方法有机地结合多任务深度神经网络与条件随机场和模板匹配算法，利用“预训练+微调+后处理”的方式实现飞机型号的高精度识别。首先，基于多任务深度神经网络迁移学习技术实现飞机目标物位置、掩膜与关键点信息识别。其次，为了便于后期高精度模板匹配，利用本文提出的融合条件随机场的飞机目标掩膜精化算法和基于关键点的姿态调整算法，实现识别目标的边界精细化与机体姿态调整；最后，在本文构建的飞机型号模板库基础上，将经过精化后处理的飞机掩膜信息与模板库进行匹配，实现飞机目标的型号识别。为了验证所提方法的有效性，本文进行了相关实验，并与传统算法及完全端到端深度学习方法进行了对比，结果表明，本文所提方法具有更高准确率，并且在实用性方面更具优势。

Abstract: Objective:Aircraft detection by deep learning is a hot field in remote sensing image analysis. However, due to the limited perspective of satellite imagery and high similarity in appearance, aircraft type recognization is still a challenging task. The existing deep learning methods cannot be satisfied with the fine-grained aircraft type recognition tasks well, which require a refined lables for datasets. Thus, for recognizing aircraft type recognition in remote sensing images, in this paper, we propose an target segmentation and key point detection integrated aircraft type recognition method. Method: The method organically combines the multi-task deep neural network with the conditional random field and template matching algorithm, and achieves the high-precision recognition of the aircraft type by means of "pre-training, fine-tuning, post-processing". First, based on the multi-task learning and transfer learning technology, the aircraft target position, mask and key point recognition are realized. Secondly, in order to facilitate the high-precision template matching in the later stage, the aircraft target mask refinement algorithm and the key-point based mask attitude adjustment algorithm are proposed to achieve the boundary refinement of the recognition target and the aircraft target mask attitude adjustment. Finally, based on the aircraft type template library constructed in this paper, the refined aircraft mask information is matched with the template library to recognize the aircraft type. Results: The algorithm was applied to the MTARSI data set and remote sensing images for verification. The results showed that the recognition accuracy of 10 types of aircraft could reach more than 85%. Aircraft with simple structure and unique shape have higher recognition accuracy, such as B-2, B-1, etc., while aircraft with complex structure and high similarity with other types of shapes have lower recognition accuracy, such as E-3 reconnaissance aircraft. The algorithm is compared with traditional algorithms and end-to-end deep learning methods, and the results show that 11 types of aircraft are 15.4% and 20.7% more accurate than the two contrasting methods, respectively. Conclusion: The use of target segmentation and key point information has achieved good results in model recognition on high-resolution remote sensing images. However, limitations remain in the breadth of identifiable aircraft types. Therefore, further research is needed.