Advances in Deep Learning Models for Satellite Image Analysis

Dear Colleagues,

Due to the growing effective model training strategies, availability of large-scale labeled data sets, and high-performance computational hardware, deep learning-based methods have recently been utilized in a wide range of applications, including remote sensing image analysis. In addition. deep learning models have emerged in recent years as a powerful solution for processing high dimensional and complex remote sensing data in a variety of tasks (classification, regression, forecasting, or clustering). Although these methods can handle massive amounts of data collected by remote sensors, they require a large number of high-quality reference data. The complexity and dimensionality of remote sensing data, which are substantial, multi-variate, noisy, and irregularly collected increase the challenges in applying deep learning techniques in different Earth Observation applications.

This Special Issue will publish review and research documents on advanced deep learning models, including but not limited to innovative CNN, graph, and vision transformer-based deep learning techniques for remote sensing applications, focusing on tasks that discuss the field's issues.

Potential topics of interest are listed below:

• Deep learning-based remote sensing image processing (image classification, object detection, semantic segmentation, pan-sharpening, image enhancement, and change detection)
• Unsupervised, semi-supervised, self-supervised, graph, adversarial, active, and transfer learning for dealing with scarcity and/or low-quality of data sets.
• Knowledge acquisition of deep learning architectures and algorithms for remote sensing images
• Novel benchmark datasets for remote sensing image interpretation
• Vision Transformer (ViT) in remote sensing

Dr. Masoud Mahdianpari
Dr. Fariba Mohammadimanesh
Dr. Ali Jamali
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.

• machine learning
• deep learning
• vision transformer
• environmental monitoring
• remote sensing