遥感图像时空融合综述

Temporal and Spatial Fusion of Remote Sensing Images:A Review

高时空分辨率的遥感图像大数据在遥感领域发挥着重要作用。然而,由于技术上和预算上的限制等原因,目前单一的卫星传感器无法获取同时具有高空间分辨率和高时间分辨率的遥感影像。因此遥感图像时空融合技术被认为是解决时间分辨率和空间分辨率折衷问题的有效途径之一。随着深度学习在各领域的广泛应用,深度学习技术已经被证实是解决图像问题非常有效的方法。针对国内外学者的研究成果,全面总结遥感图像时空融合的经典算法,同时重点分析基于深度学习的遥感图像时空融合算法的研究成果,在三个数据集上进行复现并分析实验结果,并对未来遥感图像时空融合进行展望。

Big data of remote sensing image with high temporal and spatial resolution plays an important role in remote sensing field. However, due to technique and budget constraints, a single satellite sensor cannot acquire remote sensing images with both high spatial resolution and high temporal resolution. Therefore, the temporal and spatial fusion technology of remote sensing image is regarded as one of the effective ways to solve the tradeoff between temporal resolution and spatial resolution. With the wide application of deep learning in various fields, deep learning technology has been proved to be a very effective method to solve image problems. According to the research results of scholars at home and abroad,the classical algorithm of remote sensing image spatiotemporal fusion is comprehensively summarized. Meanwhile, the research results of remote sensing image spatiotemporal fusion algorithm based on deep learning are analyzed, which are replicated on three datasets and the experimental results are analyzed, and the future of remote sensing image spatiotemporal fusion is prospected.