Multisource Remote Sensing Data Fusion and Application

Due to hardware limitations and other factors, a single remote sensing sensor generally focuses on one-side observation. Multisource remote sensing data can provide complementary information for a target or a task, such as environment and disaster monitoring, and coastal mapping. Multisource remote sensing data fusion by synthesizing the complementary information of multiple remote sensing data or other observations, along with their comprehensive processing, analysis, and decision, will contribute to obtaining higher quality data, more optimized features, and reliable knowledge.

This Research Topic focuses on multisource remote sensing data fusion and its applications. It will foster newly advanced technology for remote sensing image fusion and applications. The theme of this Research Topic mainly includes multisource remote sensing data collection (opening satellite, unmanned aerial vehicle (UAV), ground-based, social sensing data, etc.), data preprocessing, spatial-temporal-spectral fusion for resolution enhancement, heterogeneous data fusion, classification, change detection, and data fusion for applications, such as urban sustainability assessment, environmental monitoring, coastal mapping, carbon sequestration investigation.

We welcome submissions based on but not limited to:
• Multisource remote sensing data collection;
• Multisource remote sensing data preprocessing (registration, denoising, radiation correction, etc.);
• Multisource remote sensing spatial-temporal-spectral fusion (Multiview super-resolution, spatial-spectral fusion, spatial-temporal fusion, temporal-spectral fusion, integrated spatial-temporal-spectral fusion, etc.);
• Multisource remote sensing heterogeneous data fusion (SAR-optical fusion, Lidar-optical fusion, point-surface fusion, remote sensing, social sensing data fusion, satellite-UAV-ground based data fusion, etc.);
• Multisource remote sensing image classification;
• Multisource remote sensing image change detection;
• Quality evaluation for multisource remote sensing data fusion;
• Multisource remote sensing data fusion for applications (environmental monitoring, coastal mapping, urban sustainability assessment, carbon sequestration investigation, etc.).

Keywords: Multisource remote sensing, data fusion, classification, change detection, machine learning, deep learning

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.