Lightweight Artificial-Intelligence Techniques for Remote-Sensing Image Processing

Dear Colleagues,

In the era of big earth observation data, deep learning has revolutionized remote sensing image processing with remarkable performance in tasks from image restoration to scene understanding, but such gains often incur massive computational resource demands, large memory footprints and high energy consumption—posing a severe bottleneck for practical deployment, especially in resource-constrained scenarios like on-board satellite processing and real-time UAV monitoring. As the need for rapid response and edge computing grows, there is an urgent need to develop lightweight, efficient AI techniques that retain high accuracy while minimizing computational overhead, which must be deeply integrated with key remote sensing image processing tasks (including hyperspectral image analysis, lightweight network design, model compression, land cover mapping, object detection, change detection and multimodal data fusion) to address task-specific challenges and realize the practical value. This Special Issue focuses on this intersection to highlight the significance of advancing lightweight deep learning for remote sensing image processing tasks.

The primary aim of this Special Issue is to explore cutting-edge lightweight AI methodologies tailored for remote sensing imagery. We seek to address the critical challenge of deploying advanced vision models on hardware with limited power and processing capabilities. This topic aligns seamlessly with the scope of Remote Sensing, bridging the gap between theoretical algorithm design and practical engineering implementation. We invite contributions that focus on model compression, efficient architecture design, and algorithmic acceleration to enable real-time image processing, fusion, and interpretation without compromising performance.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Hyperspectral Image Analysis: Dimensionality reduction, spectral unmixing, and classification.
• Semantic Segmentation: Deep learning models for land cover and land use classification.
• Object Detection: Identification of vehicles, buildings, and ships in high-resolution aerial/satellite images.
• Change Detection: Advanced frameworks for analyzing multitemporal data.
• Multimodal Data Alignment and Fusion: Algorithms for combining Hyperspectral, Multispectral, LiDAR, and SAR data.
• Remote Sensing Image Restoration and Enhancement: Lightweight algorithms for denoising, dehazing, geometric correction, and contrast enhancement of remote sensing images.
• Model Compression Techniques: Network pruning, quantization, and knowledge distillation for satellite/aerial imagery.
• Lightweight Network Architecture Design: Mobile-friendly backbones and efficient neural operators for remote sensing.

Dr. Hao Zhang
Dr. Qiwen Jin
Dr. Chengli Peng
Dr. Xingyu Jiang
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-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.