Machine Learning and Computational Intelligence in Remote Sensing

Dear Colleagues,

Remote sensing is in an exciting technological phase, where the integration of machine learning and computational intelligence provides unprecedented opportunities for processing and analyzing large-scale remote sensing data. From satellite imagery to drone photography, the diversity and complexity of remote sensing data require us to adopt advanced algorithms and models to extract useful information and solve practical problems. With the rapid development of remote sensing technology, our needs and capabilities for Earth observation are continuously increasing. Machine learning and computational intelligence, as powerful tools, are changing the way remote sensing data are analyzed and enhancing our understanding of complex Earth systems. This Special Issue aims to gather and showcase the latest research findings in this field, promote academic exchange, and advance the science of remote sensing.

The goal of this Special Issue is to provide an interdisciplinary communication platform, to develop new machine learning algorithms adapted to the characteristics of remote sensing data. Explore automated and intelligent data preprocessing, feature extraction, and classification methods. Train and validate machine learning models using remote sensing datasets. Share application examples of machine learning in agriculture, forestry, urban development, environmental monitoring, and other fields. Articles may address, but are not limited to, the following topics:

• Application of machine learning in the fusion of multi-source remote sensing data.
• Optimization of deep learning network structures for remote sensing image classification and object recognition.
• New methods and strategies of computational intelligence in remote sensing data processing.
• Automated feature extraction and pattern recognition in remote sensing data.
• Spatiotemporal analysis and dynamic monitoring of remote sensing data.
• Applications of remote sensing data in climate change, disaster response, and sustainable development.

Dr. Zhiheng Liu
Dr. Jianhua Guo
Dr. Xiaobo Li
Prof. Dr. Suiping Zhou
Dr. Tingting Wu
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. Electronics 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 2400 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.

• machine learning
• computational intelligence
• remote sensing
• data analysis
• earth observation