ISPRS Journal of Photogrammetry and Remote Sensing
Call for papers on Theme Issue “Multi-Modal Learning in Photogrammetry and Remote Sensing”
• 大类 : 工程技术 - 1区
• 小类 : 自然地理 - 1区
• 小类 : 地球科学综合 - 1区
• 小类 : 成像科学与照相技术 - 1区
• 小类 : 遥感 - 1区
In the last decade, there have been ever-increasing amount of multi-modal data acquired from different platforms, such as airplanes, satellites, autonomous vehicles, surveillance cameras, and unmanned aerial vehicles (UAVs), for different Photogrammetry & Remote Sensing applications. However, the majority of the tasks tackled so far involve only one modality, e.g. RGB images, Lidar point clouds, infrared images, or IMU data. This is due in part to the differences in structure among modalities, which complicates their joint analysis. Another issue is the unbalanced number of labelled samples available among modalities, resulting in a significant gap in performance when algorithms are trained separately. Clearly, the Photogrammetry & Remote Sensing community has not exploited the full potential of multi-modal data. Additionally, it is undeniable that deep learning has transformed the field of computer vision, and now rivals human-level performance in certain tasks such as image recognition and semantic segmentation. In this context, there is a strong need for research and development of approaches for multi-sensory and multi-modal deep learning within the new recognition frameworks. With a special issue on "Multi-Modal Learning in Photogrammetry and Remote Sensing" we aim at fostering collaboration between the Photogrammetry & Remote Sensing and the Computer Vision& Machine Learning communities.
The “Multi-Modal Learning in Photogrammetry and Remote Sensing” theme issue deals with multi-modal data for 3D modelling, semantic interpretation, and static or dynamic scene understanding. Topics of multi-modal, multi-temporal, and multi-scale data analysis and learning are therefore of particular relevance for this theme issue:
- Multimodal learning, self-supervised/unsupervised learning for multimodal data;
- Multimodal data generation and transfer learning;
- Multimodal data fusion and data representation;
- Scene understanding from multi-sensory data: object recognition, semantic segmentation,tracking, and 3D reconstruction;
- Multimodal applications with multispectral data, hyperspectral data, airborne/terrestrial imageryand point cloud data.