Advances in High-Resolution Satellite Remote Sensing Image Processing and Classification

Dear Colleagues,

Satellite remote sensing is a major technical means for obtaining large-scale and rapid Earth observations. In recent years, global satellite Earth observation technology has flourished, with continuous improvements in the spatial resolution, temporal resolution, and spectral resolution of satellite remote sensing images. The types of satellite remote sensing have also become increasingly diverse, with multi-source, multi-modal, multi-temporal, and multi-angle satellite remote sensing images containing various geometric, radiometric, and spectral data that provide a rich source of information for scientifically analyzing changes in the Earth's surface conditions. Therefore, accurately extracting the various types of information contained in the images is crucial for the application of satellite remote sensing.

To overcome the geometric and radiometric differences in satellite remote sensing imagery and fully realize its application value in various fields of interest, such as land usage and environmental monitoring, cultural heritage, archaeology, precision farming, human activity monitoring, and other engaging research and practical areas, new relevant challenges in research fields need to be addressed, including those connected to image orientation, enhancement, registration, and fusion, 3D reconstruction, change detection, and trend analysis. In this context, automated and reliable techniques are needed to process and extract information from such a large amount of satellite remote sensing images.

Given the reasons above, the processing and classification of high-resolution satellite remote sensing images is becoming highly attractive and popular, making it possible to reach a very high degree of autonomous functioning, accuracy, and promising results, including the following applications, among others of interest:

• Geometric and radiometric processing of high-resolution satellite remote sensing images;
• Registration and fusion of multi-source and multi-modal satellite remote sensing images;
• High-precision and high-quality 3D reconstructions of satellite remote sensing images；
• Object extraction and accuracy evaluation in 3D reconstruction;
• Deep learning methods for satellite image processing and interpretation;
• Enhancement and super-resolution reconstruction for satellite remote sensing images；
• Segmentation and clustering of satellite remote sensing images；
• Automation in thematic map production (e.g., spatial and temporal pattern analysis, change detection);
• New satellite camera calibration, orientation, and 3D reconstruction;
• Change detection in complex environments (e.g., farmlands, buildings);
• Fine extraction of main food crops;
• Monitoring of phenological periods using satellite remote sensing images.

Dr. Yingdong Pi
Dr. Wenli Huang
Dr. Qingwei Zhuang
Dr. Arnab Muhuri
Guest Editors

Manuscript Submission Information

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• geometric/radiometric processing
• image registration
• image interpretation
• 3D reconstruction
• deep learning
• multi-source image fusion and information extraction
• image clustering and segmentation
• object extraction