Machine Learning for Intelligent Processing and Applications of Multi-Source Remote Sensing Data

Dear Colleagues,

With the development of remote sensing techniques and various remote sensing sensors/platforms (ground-based, UAV-based, satellite-based), information acquisition and intelligent processing technologies are showing a rapid and diversified development trend. Integrating multi-sensor data to implement comprehensive detection and analysis can compensate for the unreliability and inaccuracy of single-sensor systems in Earth observation, while machine learning technology can effectively solve various difficulties in intelligent interpretation of multi-source remote sensing data. Hence, research on machine learning for intelligent processing and applications of multi-source remote sensing data is currently a popular trend in the field of Earth observation.

This Special Issue is open to research on the intelligent processing of multi-source remote sensing data to identify current research trends and key issues, with the aim of compiling the latest research on how multi-source remote sensing data, machine learning, and other methods can effectively assist in various remote sensing applications.

This Special Issue calls for articles that focus on the development and applications of theory, processing methods, strategies, and new technologies using multi-source remote sensing data, including high-resolution visible light, panchromatic, infrared, multispectral, hyperspectral, synthetic aperture radar (SAR), light detection and ranging (LiDAR), etc. Potential topics may include, but are not limited to, the following:

• Multi-source remote sensing data imaging (e.g., super-resolution, denoising, pansharpening, data fusion, etc.);
• Cross-modal analysis in remote sensing, including spectral irradiance analysis, multi-source feature optimization, intelligent collaborative interpretation, etc.
• Multi-source remote sensing processing (e.g., registration, fusion, feature extraction, classification, segmentation, object detection, etc.);
• Machine learning methods as well as their lightweight designs for intelligent processing of multi-source remote sensing data.

Prof. Dr. Wei Li
Dr. Haiyong Gan
Prof. Dr. Heng-Chao Li
Dr. Wenshuai Hu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.