Image Processing and Analysis: Trends in Registration, Data Fusion, 3D Reconstruction, and Change Detection (Fourth Edition)

Dear Colleagues,

Satellite, aerial, UAV, and terrestrial imaging techniques are constantly evolving in terms of data volume, quality, and variety. Earth observation programmes, both public and private, are making a growing amount of multi-temporal data available—and often publicly accessible—at an increased spatial resolution and with a high revisit time. At the opposite end of the platform scale, UAVs, due to their higher flexibility, represent a new paradigm for acquiring high-resolution information with high frequencies. Similarly, consumer-grade 360° cameras and hyperspectral sensors are becoming increasingly widespread in different terrestrial platforms and applications.

Remotely sensed data can provide a basis for timely and efficient analysis in several fields such as land usage and environmental monitoring, cultural heritage, archaeology, precision farming, human activities monitoring, and others engaging research and practical fields of interest. Its increasing availability, the need for fast and reliable responses, and the increment in the number of active (but often unskilled) users all pose new relevant challenges in research fields connected to data registration, data fusion, 3D reconstruction, and change detection.  In such a context, automated and reliable techniques are needed to process and extract information from such a large amount of data.

This is the fourth edition of this Special Issue (the first edition is available at https://www.mdpi.com/journal/remotesensing/special_issues/rs_image_trends, second edition at https://www.mdpi.com/journal/remotesensing/special_issues/Image_Processing_and_Analysis_II, and third edition at  https://www.mdpi.com/journal/remotesensing/special_issues/LD04C9ZJ5V) and aims to present the latest advances in innovative image analysis and image processing techniques, as well as the contribution of these advances to a wide range of application fields in an attempt to foresee where they will lead the discipline in the upcoming years. As far as process automation is concerned, it is of the utmost importance to develop an appropriate understanding of the algorithmic implementation of the different techniques and identify their maturity as well as possible applications that might leverage their full potential. For this reason, features of particular interest may include the following: (i) Accuracy: The agreement between the reference (check) and measured data (e.g., accuracy of check point in image orientation or accuracy of testing set in data classification). (ii) Completeness: The amount of information obtained from the different methodologies and their space/time-distribution. (iii) Reliability: The algorithm’s consistency, intended as stability to noise, and the algorithm’s robustness, intended as an estimation of the reliability level of the measurements and the algorithm’s to identify gross errors. (iv) Processing speed: The algorithm’s computational load.

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

• Image registration and multi-source data integration or fusion methods;
• Deep learning methods for data classification and pattern recognition;
• Automation in thematic map production (e.g., spatial and temporal pattern analysis, change detection, and definition of specific change metrics);
• Cross-calibration of sensors and cross-validation of data/models;
• Seamless orientation of images acquired on different platforms;
• Object extraction and accuracy evaluation in 3D reconstruction, including volume-rendering methods (e.g., NeRF and Gaussian Splatting);
• Low-cost 360° and fisheye consumer-grade camera calibration, orientation, and 3D reconstruction;
• Direct georeferencing of images acquired on different platforms.

Prof. Dr. Riccardo Roncella
Dr. Mattia Previtali
Dr. Luca Perfetti
Guest Editors

Dr. Rasoul Eskandari
Guest Editor Assistant
Affiliation: Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Giuseppe Ponzio, 31, 20133 Milano, Italy
Email: rasoul.eskandari@polimi.it
Interests: image analysis; image matching; multi-view reconstruction; laser scanning; point cloud classification; monitoring
Website: https://www.dabc.polimi.it/en/staff/rasoul.eskandari

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• image registration
• change detection
• 3D reconstruction
• deep learning
• hyperspectral
• image matching
• data/sensor fusion
• object-based image analysis
• pattern recognition
• volume rendering

• Image Processing and Analysis: Trends in Registration, Data Fusion, 3D Reconstruction, and Change Detection (Third Edition) in Remote Sensing (8 articles)