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

Dear Colleagues,

Satellite, aerial, and UAV imaging are constantly evolving, both in terms of data volumes and quality. Earth observation programs, both public and private, are making available to different users (private companies, public administrations, and the scientific community) a growing amount of multi-temporal data, often publicly available, 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, are beginning to represent a new paradigm for acquiring high resolution information and can offer mapping capacities unachievable up to a few years ago. Due to this large availability of data, the scope of image-based remote sensing applications and the number of active, often unskilled, users has increased consistently in the last years: today, beside traditional application fields such as land usage and environmental monitoring, cultural heritage, archaeology, precision farming, human activities monitoring, and others are emerging research and practical fields of interest. In such fast-evolving context, automated techniques are therefore needed to process and extract relevant information from such a large amount of data.

The Special Issue focuses on presenting the latest advances of innovative image analysis and image processing techniques and their contribution in a wide range of application fields, in an attempt to foresee where they will lead the discipline and practice in the next years. As far as process automation is concerned, it is of the utmost importance to invest in an appropriate understanding of the algorithmic implementation of the different techniques and identify their maturity as well as applications where their use might leverage their full potential. For this reason, special focusing features might be (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 consistency, intended as stability to noise, and algorithm robustness, intended as estimation of the measurements’ reliability level and capability to identify gross errors; and (iv) processing speed: the algorithm computational load.

Scope includes but is not limited to the following:

• image registration and multi-source data integration or fusion methods;
• cross-calibration of sensors and cross-validation of data/models;
• orientation in a seamless way of images acquired with different platforms;
• object extraction and accuracy evaluation in 3D reconstruction;
• low-cost sensors for mapping and 3D modelling;
• automation in thematic map production (e.g., spatial and temporal pattern analysis, change detection, and definition of specific change metrics);
• automatic or semi-automatic procedures to produce down streaming operational services;
• deep learning methods for data classification and pattern recognition;

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

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