Remote Sensing Image Classification and Semantic Segmentation (Second Edition)

Dear Colleagues,

With the rapid growth in remote sensing imaging technology, vast amounts of remote sensing data are generated, which is nontrivial for land-monitoring systems, national security, agriculture, medical, atmosphere, etc., for Earth, Mars, etc. In recent decades, deep learning techniques have had a significant effect on remote sensing data processing and analysis, especially in image classification and semantic segmentation. However, several challenges still exist due to the limited number of annotated datasets, restricted computing resources, the special characteristics of different sensors or data sources, the complexity and diversity of large-scale areas and other specific problems, which make deep-learning-based algorithms more difficult in real-world applications. Therefore, the novel deep neural networks combined with few-shot learning, meta-learning, attention mechanisms or other new transformer technologies need to be given more attention, which is of vital importance in remote sensing image classification and semantic segmentation. It is also necessary to develop lightweight, explainable, and robust networks for remote image applications, especially image classification and sematic segmentation.

This Special Issue aims to develop state-of-the-art deep networks for more accurate remote sensing image classification and sematic segmentation. Furthermore, it also aims to achieve a cross-domain performance with high efficiency through a lightweight network design.

This Special Issue encourages authors to submit research articles, review articles or application-oriented articles on topics regarding remote sensing image classification, semantic segmentation, detection, spectral super-resolution and understanding-related works; these include, but are not limited to, the following topics:

• Machine/deep-learning-based algorithms;
• Remote sensing image processing and pattern recognition;
• Image classification;
• Semantic segmentation;
• Target detection/change detection;
• Image or data fusion/fusion classification;
• Lightweight deep neural networks;
• Domain-adaptation/few-shot-learning/meta-learning-based algorithms;
• Onboard real-time applications.

Dr. Jiaojiao Li
Prof. Dr. Qian Du
Prof. Dr. Jocelyn Chanussot
Prof. Dr. Wei Li
Dr. Bobo Xi
Prof. Dr. Rui Song
Prof. Dr. Yunsong Li
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 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
• deep learning
• semantic segmentation
• classification
• cross-domain
• earth observation

• Remote Sensing Image Classification and Semantic Segmentation in Remote Sensing (26 articles)