Dear Colleagues,

With the availability of large amounts of remote sensing data from different sensors, multi-modal data processing and analysis techniques have attracted increasing interest in the remote sensing and geoscience communities. However, due to the differences in imaging sensor principle and resolution between different modalities, the appropriate representation of their complementary information remains largely challenging.

In recent years, the great success of deep learning has provided an important opportunity for intelligent information extraction from multi-source data. A large number of scholars have tried to introduce deep learning methods into the field of remote sensing, the performance of which is significantly better than traditional methods. However, the very unique characteristics of remote sensing data, such as large scale, image quality affected by cloudy weather, complex image background, and multi-scale objects, make it difficult for existing deep learning methods to further improve performance. Currently, most remote sensing image interpretation methods are proposed for data of a specific modality and for specific tasks, resulting in a certain bottleneck in the development of multi-modal and multi-task learning in the field of remote sensing. Therefore, on the basis of understanding the characteristics of multi-modal remote sensing images, it is necessary to design a suitable feature extraction structure to make the model have better generalization ability to multi-modal or multi-task learning.

This Special Issue will report cutting-edge models, methods, and system tools tailored for multiple tasks in dealing with multi-modal remote sensing data. It aims at boosting the interpretation of remote sensing data towards more accurate, autonomous, and cost-effective quality levels.

The Special Issue invites authors to submit contributions in (but not limited to) the following topics:

• Multi-modal data fusion, analysis, and interpretation
• Multi-temporal remote sensing data analysis
• Multi-task learning with deep neural networks
• Multi-modal pre-training algorithms and architectures
• Image fusion for remote sensing
• Deep transfer learning methods for remote sensing
• Weakly supervised, semi-supervised and self-supervised learning methods for remote sensing
• New datasets, methods, and platforms for image segmentation, object detection and classification

Dr. Xian Sun
Dr. Hai Huang
Dr. Martin Weinmann
Prof. Dr. Helmut Mayer
Guest Editors

Mr. Peijin Wang
Guest Editor Assistant
Affiliation: Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
E-Mail:wangpj@aircas.ac.cn
Interests: remote sensing image interpretation; machine learning; computer vision

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 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind 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.

• remote sensing data
• neural networks
• image fusion
• image segmentation
• object detection
• classification
• photogrammetry