Theme and Scope Unmanned Aerial Vehicles (UAVs) with a variety of sensors can collect real-time, high-resolution sensory data of the environment in a secure manner. Construction, agriculture, entertainment, and transportation can all benefit from this information. The autonomy of the drone is important for many of these applications. In order to make sense of complex environments without requiring operator interference, UAVs must be equipped with accurate localization, navigation, and exploration capabilities. Users of UAV technology are often bombarded with massive quantities of data (e.g., large volumes of imagery). Hence there arises a critical need for intelligent algorithms for monitoring and controlling the data overflow by applying fusion and transforming different data into valuable and concise information. Radar, pseudo-satellite base stations, satellite and visual guidance are among the most widely used guidance systems today. However, achieving a near-ideal effect with only one of these approaches is difficult. Even the most commonly used satellite GPS system is highly susceptible to interference, despite the fact that it can acquire maximum location and real-time information and as a result the accuracy is not high enough. The widely used visual navigation system can only provide directions within a short distance, and the location of fast-moving objects is not precise enough. For redundant guidance, using multiple sensor data fusion is a better approach today. Many countries have begun to investigate the methods of applying multi-sensor data fusion technology to the autonomous landing guidance in UAV in recent years. This emerging new technology-oriented application in various domains have proliferated the technological advances in recent years in Unmanned Aerial Vehicle (UAV) and with regular revisit time in high-resolution satellite imagery to address security challenges on regional to global scale. Though UAV-based remote sensing is restricted in its ability to track wide areas, it is quickly becoming a prerequisite for satellite remote sensing due to its ability to create and test field-scale models which can be ranged with the scaling from regional to global scale by renovating satellite observations from UAV-based models. The realization of autonomous landing of drones is becoming highly important to obtain high-precision navigation information in such a way how to sensibly fuse the measured data of the sensors. Most of the applications, mainly those involving seasonal or rapid changes, can benefit from fused fine-resolution involving with time-series datasets. Data fusion can be used to provide periodic data for phenology monitoring while maintaining a fine spatial resolution specific to the area. For rapid change cases, the argument for fused data is potentially even stronger. The case for regular updates at fine resolution is evident in these circumstances. Although these application domains provide convincing reasons for data fusion, there are a number of obstacles to overcome, including the following: (i) Because some of the sensor’s data volumes provided at coarse resolution are already enormous, fusion of datasets would almost certainly have to be done on a case-by-case basis as like an on demand service and (ii) ultra-fast processing will be required to generate prompt change events implying that accuracy may be outweighed by speed in such cases Satellite/UAV data fusion enhanced the model performance compared to satellite or UAV data because of the complementary information given by abundant spectral features that are satellite-based and structure features derived from UAV. Consequently end-to-end construction of a low-cost efficient civilian UAV system, incorporating hardware and software, research results relating to the designing and implementation of a compact and low-cost UAV system with multi-sensor data fusion, UAV-based video data and its real-time processing, and its evaluation for civilian applications are all needed in research focus with fast-response to time critical environmental applications, such as, fire behavioural and characteristics analysis, wildfire surveillance , crop monitoring and stress detection , and Fire Search and Rescue decision making approaches. This Special Issue on Advances in Data Fusion for Monitoring and Change Detection in UAV emphasis on the emerging solutions and the state-of-the-art for the intelligent processing of data from the tiny sensors on-board with the localization and navigation of UAVs. Potential topics of interest include, but are not limited to:
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Important Dates
Supervising Editor: Dr. Wei-Che Chien Department of Computer Science & Information Engineering, Dong Hwa University, Taiwan E-mail: wcc@gms.ndhu.edu.tw Guest Editors: Dr. Manju Khari Jawaharlal Nehru University, India E-mail: manjukharii@ieee.org
Dr. Rubén González Crespo Universidad Internacional de La Rioja, Spain E-mail: ruben.gonzalez@unir.net
Dr. Hemlata Sharma Sheffield Hallam University, United Kingdom E-mail: h.sharma@shu.ac.uk |
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ISSN 1607-9264
e-ISSN 2079-4029
JCR 2022 Impact Factor: 1.6
CiteScore 2022: 2.8
SJR 2022: 0.326
SNIP 2022: 0.488
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