Deep Fusion and Analysis of Hyperspectral and Multi-Source Remote Sensing Data via Advanced AI

Dear Colleagues,

Recent advances in Earth observation technologies have led to an unprecedented increase in the availability of remote sensing data, encompassing hyperspectral, multispectral, LiDAR, and synthetic aperture radar (SAR) collected from different satellites and airborne platforms. While hyperspectral imaging technology provides rich spectral details for fine-grained ground object identification, it often exhibits insufficient spatial or temporal resolution characteristics, further restricting the comprehensive capture of ground changes. The integration of other heterogeneous remote sensing modalities can offer rich structural, geometric, and all-weather information and promote the rapid development of next-generation remote sensing applications. Advanced AI-driven techniques, including cross-modal representation learning, transfer learning, graph neural networks, self-supervised pretraining, and foundation models, are progressively advancing the state of the art in accuracy, generalization, model interpretability, and scalability. This Special Issue aims to gather high-quality research contributions that advance the theory, methodology, and application of deep fusion and intelligent analysis of hyperspectral and multi-source remote sensing data. By bringing together the latest breakthroughs in AI-driven  remote sensing technologies, more innovative and interdisciplinary solutions for intelligent Earth observation systems can be encompassed to jointly promote the interdisciplinary development of geospatial intelligence.

This Special Issue seeks to compile cutting-edge research on AI-driven fusion and analysis technologies in hyperspectral and multi-source remote sensing data, emphasizing novel algorithms and real-world applications of urban, ecology, agriculture, and environmental science. By capturing multi-source data across various dimensions, more ground information can be fully utilized to bring out their maximum value in remote sensing applications.

The scope of this Special Issue includes, but is not limited to, the following topics:

• Fusion algorithms of hyperspectral and multi-source remote sensing data for enhanced accuracy.
• High-precision analysis technologies on unmixing, detection, classification, segmentation, and time-series prediction.
• Cross-modal retrieval and alignment for few-shot and unsupervised learning on real-world applications.
• Multi-source and multi-scale remote sensing datasets and benchmarks for specific tasks.
• Large-language and multi-agent advanced AI-driven foundation models on multi-source remote sensing data.

Dr. Zhu Han
Dr. Longfei Ren
Dr. Xu Sun
Dr. Minghua Wang
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.