Multi-Sensor Data Fusion of Unmanned Aerial Vehicles (UAVs) Remote Sensing for Environmental Applications

Dear Colleagues,

This Special Issue focuses on the use of multi-sensor imagery from unmanned aerial vehicles (UAVs) in environmental applications. Papers dealing with the issue of data fusion in the mapping and monitoring of vegetation are very welcome. The development of artificial intelligence (AI) and autonomous systems have increased the need for gathering accurate information about our surrounding environment in the recent decade. The application of UAV sensors and platforms for remote sensing and mapping the environment has received considerable attention among researchers due to the high spatial resolution, flexibility in the acquisition and sensor integration and cost-effectiveness of UAVs compared with manned aircraft. An UAV can be equipped with different sensors based on the desired task. A sensor fusion algorithm can be used to improve accuracy and thus retrieve a better image of the searched area. The use of UAVs offers new possibilities in vegetation classification and monitoring with very high levels of spatial and temporal detail. The increasingly widespread use of the geographic object-based image analysis (GEOBIA) approach in processing allows researchers to address the high spectral variability of the ultra-high-resolution imagery provided by UAVs. Machine learning algorithms and proper segmentation algorithms and software suites can significantly improve the speed and the quality of mapping and monitoring of vegetation.

The cooperation of a group of UAVs has also been considered as a solution for searching and mapping a large area that presents new challenges in the control of UAVs and for gathering and processing huge amounts of data collected from different sensors and UAVs. In addition, the detection and elimination of faulty information to improve the accuracy of remote sensing are new challenges that need to be addressed.

In this Special Issue, researchers are encouraged to submit valuable research findings that address the mentioned issues from a wide variety of perspectives. Welcomed topics include but are not limited to:

• novel sensor fusion algorithms applied in UAVs for remote sensing;
• fault detection and elimination from the gathered data in UAV’s sensors;
• data processing/mining algorithms to label and interpret information received from sensors in UAV;
• data fusion;
• machine learning algorithms;
• geographic object-based image analysis (GEOBIA) approach;
• cooperative control of UAVs doing a remote sensing task;
• cyberphysical threats and solutions for remote sensing using UAVs;
• mapping and monitoring natural environments with UAV remote sensing;
• co-registration of UAV imagery in monitoring tasks

Prof. Dr. Giuseppe Modica
Dr. Alireza Abbaspour
Guest Editors

Manuscript Submission Information

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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.

• UAV remote sensing
• GEOBIA co-registration
• segmentation algorithms
• sensor fusion
• sensor faults
• cooperative monitoring
• sensor data processing
• sensor spoofing