Advances on Multimodal Signal Processing: Theory and Applications

Topic Information

Dear Colleagues,

Remote sensing is generally defined as the acquisition of information on targets of interest (e.g., objects and/or phenomenon) without physically contacting it. Traditionally, this is mainly based on the electromagnetic radiation emitted or reflected by the targets of interest and such signals are acquired by expensive sensors mounted on transportation platforms, e.g., satellites and aircrafts, which are not widely accessible. With the recent rapid advancement of technologies, many relatively more lightweight and sophisticated but yet more affordable sensors are developed. This coupled with increasingly more accessible new platforms, e.g., unmanned aerial vehicles, and high performance computing facilities have enabled more remotely sensed data, and in many applications various modalities of such data, to be acquired and processed to assist in delivering better decisions for each of these remote sensing applications.

The challenges imposed by the emerging interest and need for multimodal signal processing in remote sensing are in all its key stages, i.e., acquisition, processing, extraction and interpretation/analysis of multimodal and multi-temporal data. The modalities of interest include 2D optical images, 3D optical images (e.g., RGB, multispectral and hyperspectral data) and 3D unstructured point-cloud data (e.g., LiDAR).

This Topic will include high-quality research papers, work in progress papers, surveys, real-world application/deployment studies that discuss the theories and applications of addressing these challenges. Potential topics of interest for this Topic (but are not limited to) are:

• Design, calibration and setup of multimodal remote sensing systems
• Multimodal data processing, including techniques on segmentation, reconstruction, restoration, fusion, and registration
• Multimodal image classification
• Machine learning (including deep learning, multitask learning, and transfer learning) for multimodal remotely sensed data
• Novel benchmark multisource datasets
• Emerging applications on multimodal remote sensing

Dr. Shyh Wei Teng
Topic Editor

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700
Sensors sensors	3.9	6.8	2001	17 Days	CHF 2600

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers