面向目标检测的双驱自适应遥感图像超分重建方法

有光学遥感图像超分重建方法主要是生成视觉上令人满意的图像，并未考虑后续目标检测任务的特殊性，不能有效地应用到目标检测中。基于此，提出了面向目标检测的双驱动自适应多尺度光学遥感图像超分重建方法，将超分重建网络和目标检测网络结合起来，进行联合优化。针对光学遥感图像的特点设计了自适应多尺度遥感图像超分重建网络，集成选择性内核网络和自适应特征门控单元来特征提取和融合，重建出初步遥感图像。通过提出的双驱动模块，将特征先验驱动损失和任务驱动损失传到超分重建网络中，提高目标检测的性能。在UCAS-AOD和NWPU VHR-10数据集上进行实验，并与5种主流方法进行比较，所提方法的峰值信噪比和平均准确率相较于FDSR方法分别提高了1.86 dB和3.73%。实验结果表明，所提方法和光学遥感图像目标检测结合可以取得更好的效果，综合性能更佳。 

Double drive adaptive super-resolution reconstruction method of remote sensing images for object detection

The existing optical remote sensing image super-resolution reconstruction method is mainly to generate visually satisfactory images, and does not take into account the particularity of the subsequent target detection task, so it cannot be effectively applied to target detection. Therefore, a double drive adaptive multi-scale optical remote sensing image super-resolution reconstruction method for target detection is proposed. The super-resolution reconstruction network and target detection network are combined for joint optimization. According to the characteristics of optical remote sensing image, an adaptive multi-scale remote sensing image super-partition reconstruction network is designed. The selective kernel network and adaptive gating unit are integrated to extract and fuse features, and the primary remote sensing image is reconstructed. Through the double drive module, and task driven will feature a priori driver loses to the above points in the network, on the one hand, improve the performance of target detection. The proposed method was tested on UCAS-AOD and NWPU VHR-10 datasets, and compared with the five mainstream algorithms, peak signal-to-noise ratio and average accuracy improved by 1.86 dB and 3.73%, respectively, compared with the FDSR algorithm. Experimental results show that compared with other methods, the combination of the proposed algorithm and optical remote sensing image target detection can achieve better results and the comprehensive performance is the best.