Advances and Challenges on Multisource Remote Sensing Image Fusion: Datasets, New Technologies, and Applications

Dear Colleagues,

Today, various types of remote sensing images have been developed, including optical/near-infrared satellite images, SAR images, LiDAR intensity/depth images, thermal images, vector map images, etc. Each source of images encodes one aspect of information, and thus, the fusion of different sources of images is conducive to the comprehensive utilization of their advantages. For example, SAR data can be effectively applied to geometrically orient optical satellite images without additional control points, exploiting the high geopositioning accuracy of SAR data. Image fusion has been researched for decades, and a range of techniques related to photogrammetry, computer vision, and artificial intelligence have been developed. However, accurate multisource remote sensing image fusion is still challenging, due to: (1) large nonlinear intensity differences between the different sources of images; (2) ineffective rotation-handling strategies; (3) the absence of large-scale, multisource remote sensing image datasets that include various types of images; and (4) a lack of remote sensing images oriented to deep learning frameworks which consider the spectral characteristics, geoscientific prior knowledge, etc. The topics of this Special Issue include, but are not limited to:

• Large-scale, multisource remote sensing image datasets;
• Multisource remote sensing data fusion methods that are robust when it comes to scale, rotation, and nonlinear radiation- change;
• Machine learning (including deep learning, multitask learning, and transfer learning) for multisource remotely sensed images;
• Multisource image fusion for remote sensing applications, including, but not limited to, geometric orientation, 3D reconstruction, change detection, segmentation, etc.

Dr. Yuxuan Liu
Prof. Dr. Li Zhang
Prof. Dr. Paul Rosin
Dr. Oktay Karakus
Dr. Zhihua Hu
Guest Editors

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