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
Routing Protocol for Low Power and Lossy Network
The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN.
  • 4.4K
  • 19 May 2022
Topic Review
TECuidamos
TECuidamos has four main modules: Data Acquisition, Wireless Transmission Network, Cloud Server, and User Access.
  • 619
  • 05 May 2022
Topic Review
Machine Learning Model in HSI-AD
In the field of remote sensing, hyperspectral image (HSI) is a ground image collected by advanced sensor technology and imaging system mounted on satellites or other aircraft. Anomaly detection (AD) is a very important sub-branch in machine learning and has important applications in computer vision, data mining, and natural language processing (NLP). HSI-AD refers to the identification of pixels whose spectral characteristics in an image are significantly different from adjacent or global background pixels.
  • 997
  • 05 May 2022
Topic Review
Mobile Computing for Pest and Disease Management
The demand for mobile applications in agriculture is increasing as smartphones are continuously developed and used for many purposes; one of them is managing pests and diseases in crops. Using mobile applications, farmers can detect early infection and improve the specified treatment and precautions to prevent further infection from occurring. Furthermore, farmers can communicate with agricultural authorities to manage their farm from home, and efficiently obtain information such as the spectral signature of crops. Therefore, the spectral signature can be used as a reference to detect pests and diseases with a hyperspectral sensor more efficiently than the conventional method, which takes more time to monitor the entire crop field. 
  • 1.4K
  • 28 Apr 2022
Topic Review
Telemonitoring of Continuous Positive Airway Pressure-Treated Patients
Obstructive sleep apnea–hypopnea (OSA) syndrome is a highly prevalent disease despite still being under-diagnosed. This disorder is responsible for reduced quality of life (QoL), secondary excessive daytime sleepiness (EDS), negative cognitive and psychological impacts, and contributes to risk of cardiovascular disease, stroke, and diabetes. Continuous positive airway pressure (CPAP) telemonitoring (TMg) has become widely implemented in routine clinical care. Objective measures of CPAP compliance, residual respiratory events, and leaks can be easily monitored, but limitations exist. 
  • 688
  • 25 Apr 2022
Topic Review
Distributed Machine Learning in Edge Computing
Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics).
  • 3.5K
  • 20 Apr 2022
Topic Review
Sign Language Recognition Models
A hybrid system for sign language is a combination of both vision-sensor-based and combination of different sensors based models. 
  • 3.2K
  • 01 Jun 2022
Topic Review
Modern Small Farming and Homesteading: Automate and Simplify
Since the outbreak of COVID-19 pandemic, increasing number of people from the urban environment have moved to rural areas in search for more peaceful and healthier lifestyle. They do keep their city jobs, mostly working online, and aren't interested into agriculture besides limited production for their own needs. However, as they are accustomed to the use of ICT and electronics in everyday life, they would appreciate any device that would robotize, automate or simplify common jobs on property maintenance, which in turn would provide additional time for other activities. Despite the fact that the application of WSN and IoT in agriculture has been researched for 20 years, and although there has been a significant breakthrough in agricultural robotics, the automation of small farming machines is still scarce, which may prove to be the next new big thing for small equipment manufacturers and startups.
  • 1.5K
  • 11 Apr 2022
Topic Review
Plant Viral Disease Detection
Plant viral diseases result in productivity and economic losses to agriculture, necessitating accurate detection for effective control. Lab-based molecular testing is the gold standard for providing reliable and accurate diagnostics; however, these tests are expensive, time-consuming, and labour-intensive, especially at the field-scale with a large number of samples. Recent advances in optical remote sensing offer tremendous potential for non-destructive diagnostics of plant viral diseases at large spatial scales.
  • 1.9K
  • 08 Apr 2022
Topic Review
Data Fusion in Agriculture
The term “data fusion” can be defined as “the process of combining data from multiple sources to produce more accurate, consistent, and concise information than that provided by any individual data source”. Other stricter definitions do exist to better fit narrower contexts. This type of approach has been applied to agricultural problems since the first half of the 1990s, and there has been an increase in the use of this approach. Arguably, the main challenge involved in the use of data fusion techniques involves finding the best approach to fully explore the synergy and complementarities that potentially exist between different types of data and data sources.
  • 863
  • 07 Apr 2022
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