Summary

With the growth of satellite and airborne-based platforms, remote sensing is gaining increasing attention in recent decades. Every day, sensors acquire data with different modalities and several resolutions. Leveraging on their complementary properties is a key scientific challenge, usually called remote sensing data fusion. Data fusion can be performed at three different processing levels: 1) pixel-based or raw level; 2) object-based or feature level; 3) decision level. Fusion at pixel level is often called image fusion. It means fusion at the lowest processing level referring to the merging of digital numbers or measured physical quantities. It uses co-registered raster data acquired by different sources. The co-registration step is of crucial importance because misregistration usually causes evident artifacts. Fusion at feature level requires the extraction of objects recognized in several sources of data. This is the goal of this entry collection, which will focus both on methodological and practical aspects of remote sensing data fusion.

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Topic Review
Applications and Outlook of Soil Moisture Products
Soil moisture (SM), the moisture content in the soil, is a crucial component in the hydrological cycle; it links atmospheric precipitation and underground water and is also an important parameter of energy exchange between the land surface and the atmosphere. Consequently, SM is recognized as an essential element in studies aimed at analyzing and understanding Earth system processes, such as climate change and ecological evolution. Specifically, the available water content, which is essential for vegetation growth, is one of the most important components of soil and has crucial guiding significance for agricultural production. Currently, both ground and spaceborne sensors are used to derive the original SM information. Numerous technologies, such as statistical models, data fusion, machine learning, and assimilation approaches, are widely used to improve SM quality. Additionally, SM datasets with high spatial-temporal resolution are valuable for boosting agricultural production in terms of drought and flood monitoring, crop growth analysis, and yield estimation.
  • 612
  • 11 Aug 2022
Topic Review
Livestock Management by Unmanned Aerial Vehicles
Unmanned Aerial Vehicles (UAVs) can revolutionize livestock herding and management. As a result, there is an increasing scientific interest in using UAVs to manage livestock. UAVs can be used to control livestock grazing areas and remote sensing of these animals.
  • 1.3K
  • 04 Aug 2022
Topic Review
Microseismic Monitoring and Analysis with Cutting-Edge Technology
Microseismic monitoring is a useful enabler for reservoir characterization without which the information on the effects of reservoir operations such as hydraulic fracturing, enhanced oil recovery, carbon dioxide, or natural gas geological storage would be obscured. The global energy demand is projected to increase. To meet the increasing energy demand requires new technologies to exploit unconventional reserves. Similarly, calls for climate actions such as carbon geosequestration, hydrogen generation, and geological hydrogen storage will require an improvement in reservoir characterization methods.
  • 580
  • 02 Aug 2022
Topic Review
Sustainable Wireless Sensor Network Based Environmental Monitoring
Wireless sensor networks (WSN) are the base of the Internet of Things (IoT) that all together give rise to the smart city. These WSNs consist of several sensors, which are densely distributed to observe physical or environmental conditions, like humidity, temperature, light intensity, and gas concertation. The sensors reading data are transmitted to the network coordinator, the IP-gateway, which is at the heart of the wireless network.
  • 1.5K
  • 27 Jul 2022
Topic Review
Unmanned Aerial Vehicles and Federated Learning
Unmanned aerial vehicles (UAVs) have gained increasing attention in boosting the performance of conventional networks due to their small size, high efficiency, low cost, and autonomously nature. The amalgamation of UAVs with both distributed/collaborative Deep Learning (DL) algorithms, such as Federated Learning (FL), and Blockchain technology have ushered in a new paradigm of Secure Multi-Access Edge Computing (S-MEC). Indeed, FL enables UAV devices to leverage their sensed data to build local DL models. The latter are then sent to a central node, e.g., S-MEC node, for aggregation, in order to generate a global DL model. Therefore, FL enables UAV devices to collaborate during several FL rounds in generating a learning model, while avoiding to share their local data, and thus ensuring UAVs’ privacy.
  • 963
  • 22 Jul 2022
Topic Review
Balanced Learning for Road Crack Segmentation
Road crack segmentation based on high-resolution images is an important task in road service maintenance. The undamaged road surface area is much larger than the damaged area on a highway. This imbalanced situation yields poor road crack segmentation performance for convolutional neural networks.
  • 708
  • 22 Jul 2022
Topic Review
Spectral Reconstruction Methods for Remote Sensing Images
Spectral reconstruction of remote sensing images mainly focused on RGB or multispectral to hyperspectral. Spectral reconstruction methods can be divided into two branches: prior-driven and data-driven methods. Earlier researchers adopted the sparse dictionary method. With the development of deep learning, owing to its excellent feature extraction and reconstruction capabilities, more and more researchers are adopting deep learning methods to gradually replace the traditional sparse dictionary approach.
  • 788
  • 20 Jul 2022
Topic Review
Clustering Based Optimal Cluster Head Selection
The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT.
  • 731
  • 15 Jul 2022
Topic Review
Rooftop Solar Photovoltaic Systems
Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Retrofitting distributed rooftops with solar PV is an effective means of promoting “carbon peaking” and “carbon neutral” strategies. Rooftop solar PV is geographically unrestricted. The PV cells can be closely integrated into buildings without taking up additional land resources, not only saving land resources but also improving their utilization rate.
  • 803
  • 12 Jul 2022
Topic Review
Vision-Based Autonomous Vehicle Systems
Autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. Deep learning is fast becoming a successful alternative approach for perception-based AVS as it reduces both cost and dependency on sensor fusion.
  • 1.0K
  • 12 Jul 2022
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