Vehicle target recognition algorithm for UAV image based on DRFP

针对无人机在复杂战场环境的侦察任务中,目标在视场中尺寸过小、边缘和纹理信息较少所造成的目标识别难题,提出一种新的基于深度学习的单阶段目标识别网络DRFP。DRFP网络以残差结构为骨架,使用特征金字塔结构实现特征融合;其次在损失函数中使用添加了调整因子的交叉熵函数,实现对难样本的重点关注、训练;最后使用高斯型非极大值抑制算法(G-NMS),提高目标密集区检出率。使用无人机航拍图像数据集进行地面车辆目标识别的实验结果表明:所提出的单阶段模型的精度(mAP值)为83.16%,达到了两阶段网络模型的水平;同时,识别速度符合实时性的要求。</p

Dual efficient self-attention network for multi-target detection in aerial imagery

Aerial imagery target detection has been widely used in the military and economic fields. However, it still faces a variety of challenges. In this paper, we proposed several efficiency improvements based on YOLO v3 framework for getting a better small target detection precision. Firstly, a dual self-attention (DAN) block is embedded in Darknet-53's ResNet units to refine the feature map adaptively. Furthermore, the deep semantic features are cascaded with the shallow outline features in a feedforward deconvolutional module to obtain context details of small targets. Finally, introducing online hard examples mining and combining Focal Loss to enhance the discriminating ability between classes. The experimental results on the VEDAI aerial dataset show that the proposed algorithm is significantly improved in accuracy compared to the original network and achieves better performance than two-stage algorithms

国土环境时空信息分析与数字地球相关理论技术预研究

发展了利用遥感技术重建历史时期国土环境状况空间数据库的重要技术方法,并依此建立了覆盖全国的TM影像数据库、恢复了我国80年代中后期的土地利用空间数据库,制图精度为十万分之一。该数据库和以前建成的90年代中期、末期的国土资源遥感时空数据库集成并构成了我国国土资源遥感时空数据系列。开展了我国土地利用/土地覆盖近10年的时空过程研究,首次揭示了我国土地利用/土地覆盖近10年来的动态格局及主要驱动力。开发了海量数据管理与多分辨率无缝影像数据库技术及时空分析技术,并以此开发了具有自主知识产权的大型国产化GIS软件包SuperMap,在地理空间信息认知、地球信息图谱、大型空间数据库知识挖掘和空间分析模型反..