摘要
超分辨率重建技术在卫星遥感图像信息智能处理领域中有重要的应用。现有面向遥感图像超分辨率重建的深度学习方法大多只能处理一种比例因子的超分辨率重建任务,在多尺度层面上缺少泛化性,难以满足真实遥感图像多倍率连续放大的超分辨率重建任务需求。为解决遥感图像超分辨率重建过程中的多尺度放大问题,本文采用元学习的方法,在构建单一自适应模型的基础上实现对遥感图像的任意尺度超分辨率重建,提升遥感图像的空间分辨率,利用密集残差网络和通道注意力机制重建遥感图像中地物纹理、目标边缘等丰富细节信息。在真实遥感图像上的定量实验表明,本文所提方法重建结果的峰值信噪比能达到40 dB以上,同时在多种数据上的定量和定性实验结果证明了本文方法的有效性。
Super-resolution reconstruction technology plays an important role in the intelligent processing of satellite remote sensing images.Existing deep learning methods for super-resolution remote sensing image reconstruction can only handle super-resolution tasks with a single scale factor,lacking generalization at the multiscale level and failing to meet the requirements of real super-resolution remote sensing image reconstruction for continuous zooming at multiple magnification levels.To address the problem of arbitrary-scale super-resolution reconstruction and enhance the quality of reconstructed real remote sensing images,this paper proposes a super-resolution reconstruction method called the Meta-RDCAN,which utilizes meta-learning and residual dense channel attention network.The proposed method employs a meta-upscale module that incorporates three functions:weight prediction,location projection,and feature mapping.The module adaptively adjusts the internal parameters of a model according to different scale factors for arbitrary-scale super-resolution reconstruction.From the perspective of extracting detailed information of local land objects in a remote sensing image,a dense residual network with an attention mechanism is used as a feature extractor,enabling the reconstructed results to possess clear and distinguishable details.Extensive experiments are conducted on various datasets,including DIV2K,AID,UCMerced,WIDS,Set5,and real remote sensing image from Macao Science Popularization Satellite.The influence of variations in spatial resolution on the super-resolution reconstruction results is analyzed,and the effectiveness of the training scheme,which involves pretraining on a general dataset followed by fine-tuning on a remote sensing dataset,is validated using the loss curve.The test results with different scale factors demonstrate that the proposed model is suitable for arbitrary-scale super-resolution reconstruction tasks in remote sensing images,having scale factors of up to 4.0.Comparative experimental resul
作者
魏小源
孟钢
张浩鹏
姜志国
WEI Xiaoyuan;MENG Gang;ZHANG Haopeng;JIANG Zhiguo(School of Astronautics,Beihang University,Beijing 102206,China;Beijing Institute of Remote Sensing Information,Beijing 100192,China)
出处
《遥感学报》
EI
CSCD
北大核心
2024年第7期1735-1745,共11页
NATIONAL REMOTE SENSING BULLETIN
基金
国家自然科学基金(编号:62271017)。
关键词
超分辨率重建
遥感图像
任意尺度
元学习
密集残差网络
通道注意力机制
super-resolution reconstruction
remote sensing image
arbitrary-scale
meta-learning
residual dense network
channel attention mechanism
