Intelligent Processing of Spatial–Spectral–Temporal Remote Sensing Imagery

Dear Colleagues,

With the rapid advancement of Earth observation constellations, remote sensing has entered an era of massive multi-dimensional big data. Contemporary sensors simultaneously capture intricate spatial structures, rich spectral signatures, and continuous temporal dynamics. Effectively processing this coupled spatial–spectral–temporal imagery is paramount for critical global applications such as environmental monitoring, disaster response, and agricultural management. However, the inherent geometric distortions, atmospheric degradations, and high-dimensional non-linearity of these data pose significant modeling bottlenecks. Consequently, developing advanced intelligent processing paradigms—including deep learning, foundation models, and physically constrained neural networks—has become a vital frontier in modern remote sensing.

Although deep learning has been widely adopted, several issues remain insufficiently addressed: the lack of explicit physical constraints in data‑driven models, limited generalization across different sensors and geographic regions, and inadequate joint exploitation of the spatial, spectral, and temporal dimensions. This Special Issue aims to focus on these aspects, which are often overlooked in conventional benchmark‑oriented studies.

This Special Issue aims to gather pioneering methodologies and theoretical breakthroughs that bridge advanced artificial intelligence with multi-dimensional remote sensing physics. We seek novel frameworks that are capable of reconstructing, representing, and analyzing coupled spatial–spectral–temporal imagery under complex, real-world constraints. In particular, we encourage contributions that incorporate physical knowledge into learning systems, improve cross‑domain generalization, or jointly model spatial–spectral–temporal relationships—areas that are not yet fully explored in the current literature. This theme aligns perfectly with the journal’s scope of advancing cutting-edge machine learning architectures, statistical signal processing, and automated interpretation systems for complex Earth observation datasets.

We welcome both high-quality original research articles and comprehensive review papers. Suggested themes for submission include, but are not limited to, the following:

• Intelligent reconstruction, denoising, and super-resolution of multidimensional remote sensing imagery.
• Self-supervised, unsupervised, and zero-shot learning paradigms for unlabeled scenarios.
• Remote sensing foundation models, multi-modal learning, and vision-language cross-modal alignment.
• Time-series analysis, dynamic change detection, and spatial–spectral–temporal image fusion.
• Downstream intelligent applications, including fine-grained classification, spectral unmixing
• Object recognition, detection, and tracking.

Dr. Erting Pan
Dr. Shanhong Liu
Dr. Zhaobing Qiu
Dr. Meiqi Hu
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.