Advancements in Deep Learning for Object Detection and Segmentation in Remote Sensing Imagery

Dear Colleagues,

Deep learning has caused one of the most prominent revolutions in signal processing and computer vision, enabling accurate and fast analysis of large and complex data sets. The incorporation of these techniques in studies on digital processing of remote sensing images has allowed them to surpass traditional methods by a wide margin, considering several aspects: their ability to handle large volumes of data, the automation of complex processes, their adaptability to different types of images (with different spatial, spectral and temporal resolutions), and their accuracy in detecting subtle patterns and segmenting objects in heterogeneous scenarios and various environmental conditions. Therefore, these new algorithms have led remote sensing studies in new directions, creating diverse applications in managing Earth resources with unprecedented detail and scale.

This Special Issue aims to contribute to innovation and improving algorithms or practical applications of these techniques in several specialized fields, such as environmental monitoring, water resource management, precision agriculture, climate change analysis, natural resource management, urban planning, and disasters. Therefore, this Special Issue seeks to explore the different deep learning methods and architectures applied to satellite images, including Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Transformer-based models. In addition, we encourage submissions that address hybrid techniques and innovative approaches such as semi-supervised learning, weakly supervised learning, self-supervised learning, few-shot learning, transfer learning, multimodal data fusion, and ensemble methods.

Suggested themes and article types for submissions.

Submissions are invited to explore, but are not restricted to, the following topics:

• The development and optimization of deep learning models;
• Practical applications of deep learning in various domains;
• Multimodal data fusion;
• Semi-supervised and weakly supervised learning; Transfer learning and domain adaptation;
• Image segmentation (semantic, instance, panoptic);
• Object recognition and detection;
• Hybrid techniques and innovative methods;
• Case studies and real-world applications;
• Challenges and opportunities in the use of deep learning in remote sensing.

Dr. Osmar Abílio De Carvalho Júnior
Dr. Linlin Xu
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.