A Multi Views Approach for Remote Sensing Fusion Based on Spectral, Spatial and Temporal Information

The objectives of this chapter are to contribute to the apprehension of image fusion approaches including concepts definition, techniques ethics and results assessment. It is structured in five sections. Following this introduction, a definition of image fusion provides involved fundamental concepts. Respectively, we explain cases in which image fusion might be useful. Most existing techniques and architectures are reviewed and classified in the third section. In fourth section, we focuses heavily on algorithms based on multi-views approach, we compares and analyses the process model and algorithms including advantages, limitations and applicability of each view. The last part of the chapter summarized the benefits and limitations of a multi-view approach image fusion; it gives some recommendations on the effectiveness and the performance of these methods. These recommendations, based on a comprehensive study and meaningful quantitative metrics, evaluate various proposed views by applying them to various environmental applications with different remotely sensed images coming from different sensors. In the concluding section, we fence the chapter with a summary and recommendations for future researches

Detection/estimation of the modulus of a vector. Application to point source detection in polarization data

Given a set of images, whose pixel values can be considered as the components of a vector, it is interesting to estimate the modulus of such a vector in some localised areas corresponding to a compact signal. For instance, the detection/estimation of a polarized signal in compact sources immersed in a background is relevant in some fields like astrophysics. We develop two different techniques, one based on the Neyman-Pearson lemma, the Neyman-Pearson filter (NPF), and another based on prefiltering-before-fusion, the filtered fusion (FF), to deal with the problem of detection of the source and estimation of the polarization given two or three images corresponding to the different components of polarization (two for linear polarization, three including circular polarization). For the case of linear polarization, we have performed numerical simulations on two-dimensional patches to test these filters following two different approaches (a blind and a non-blind detection), considering extragalactic point sources immersed in cosmic microwave background (CMB) and non-stationary noise with the conditions of the 70 GHz \emph{Planck} channel. The FF outperforms the NPF, especially for low fluxes. We can detect with the FF extragalactic sources in a high noise zone with fluxes >= (0.42,0.36) Jy for (blind/non-blind) detection and in a low noise zone with fluxes >= (0.22,0.18) Jy for (blind/non-blind) detection with low errors in the estimated flux and position.Comment: 11 pages, 5 figure

Ecosystem Monitoring and Port Surveillance Systems

International audienceIn this project, we should build up a novel system able to perform a sustainable and long term monitoring coastal marine ecosystems and enhance port surveillance capability. The outcomes will be based on the analysis, classification and the fusion of a variety of heterogeneous data collected using different sensors (hydrophones, sonars, various camera types, etc). This manuscript introduces the identified approaches and the system structure. In addition, it focuses on developed techniques and concepts to deal with several problems related to our project. The new system will address the shortcomings of traditional approaches based on measuring environmental parameters which are expensive and fail to provide adequate large-scale monitoring. More efficient monitoring will also enable improved analysis of climate change, and provide knowledge informing the civil authority's economic relationship with its coastal marine ecosystems

Wireless Sensor Networks for Ecosystem Monitoring & Port Surveillance

International audienceProviding a wide variety of the most up - to - date innovations in sensor technology and sensor networks, our current project should achieve two major goals. The first goal covers various issues related to the public maritime transport safety and security, such as the coastal and port surveillance systems. While the second one w ill improve the capacity of public authorities to develop and implement smart environment policies by monitoring the shallow coastal water ecosystems. At this stage of our project, a surveillance platform has been already installed near the "Molène Island" which is a small but the largest island of an archipelago of many islands located off the West coast of Brittany in North Western France. Our final objective is to add various sensors as well as to design, develop and implement new algorithms to extend th e capacity of the existing platform and reach the goals of our project. Finally, this manuscript introduces the identified approaches as well as t he second phase of the project which consists in analyzing living underwater micro - organisms (the population o f Marine Micro - Organisms, i.e. MMOs such as Phytoplankton and Zooplankton micro - zooplankton, but also heterotrophic bacterioplankton) in order to predict the health conditions of the macro - environment s . In addition, this communication discusses developed t echniques and concepts to deal with several practical problems related to our project. Some results are given and the whole system architecture is briefly described. This manuscript will also addresses the national benefit of such projects in the case of t hree different countries (Australia, France and KS

Multimodal Data Fusion: An Overview of Methods, Challenges and Prospects

International audienceIn various disciplines, information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments or subjects, among others. We use the term "modality" for each such acquisition framework. Due to the rich characteristics of natural phenomena, it is rare that a single modality provides complete knowledge of the phenomenon of interest. The increasing availability of several modalities reporting on the same system introduces new degrees of freedom, which raise questions beyond those related to exploiting each modality separately. As we argue, many of these questions, or "challenges" , are common to multiple domains. This paper deals with two key questions: "why we need data fusion" and "how we perform it". The first question is motivated by numerous examples in science and technology, followed by a mathematical framework that showcases some of the benefits that data fusion provides. In order to address the second question, "diversity" is introduced as a key concept, and a number of data-driven solutions based on matrix and tensor decompositions are discussed, emphasizing how they account for diversity across the datasets. The aim of this paper is to provide the reader, regardless of his or her community of origin, with a taste of the vastness of the field, the prospects and opportunities that it holds

Polarization of the WMAP Point Sources

The detection of polarized sources in the WMAP 5-year data is a very difficult task. The maps are dominated by instrumental noise and only a handful of sources show up as clear peaks in the Q and U maps. Optimal linear filters applied at the position of known bright sources detect with a high level of significance a polarized flux P from many more sources, but estimates of P are liable to biases. Using a new technique, named the "filtered fusion technique", we have detected in polarization, with a significance level greater than 99.99% in at least one WMAP channel, 22 objects, 5 of which, however, do not have a plausible low radio frequency counterpart and are therefore doubtful. Estimated polarized fluxes P < 400 mJy at 23 GHz were found to be severely affected by the Eddington bias. The corresponding polarized flux limit for Planck/LFI at 30 GHz, obtained via realistic simulations, is 300 mJy. We have also obtained statistical estimates of, or upper limits to the mean polarization degrees of bright WMAP sources at 23, 33, 41, and 61 GHz, finding that they are of a few percent.Comment: 10 pages, 6 figures. Accepted for publication in Ap

Classification of non-heat generating outdoor objects in thermal scenes for autonomous robots

We have designed and implemented a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. In the context of this research, non-heat generating objects are defined as objects that are not a source for their own emission of thermal energy, and so exclude people, animals, vehicles, etc. The resulting classification model complements an autonomous bot\u27s situational awareness by providing the ability to classify smaller structures commonly found in the immediate operational environment. Since GPS depends on the availability of satellites and onboard terrain maps which are often unable to include enough detail for smaller structures found in an operational environment, bots will require the ability to make decisions such as go through the hedges or go around the brick wall. A thermal infrared imaging modality mounted on a small mobile bot is a favorable choice for receiving enough detailed information to automatically interpret objects at close ranges while unobtrusively traveling alongside pedestrians. The classification of indoor objects and heat generating objects in thermal scenes is a solved problem. A missing and essential piece in the literature has been research involving the automatic characterization of non-heat generating objects in outdoor environments using a thermal infrared imaging modality for mobile bots. Seeking to classify non-heat generating objects in outdoor environments using a thermal infrared imaging system is a complex problem due to the variation of radiance emitted from the objects as a result of the diurnal cycle of solar energy. The model that we present will allow bots to see beyond vision to autonomously assess the physical nature of the surrounding structures for making decisions without the need for an interpretation by humans.;Our approach is an application of Bayesian statistical pattern classification where learning involves labeled classes of data (supervised classification), assumes no formal structure regarding the density of the data in the classes (nonparametric density estimation), and makes direct use of prior knowledge regarding an object class\u27s existence in a bot\u27s immediate area of operation when making decisions regarding class assignments for unknown objects. We have used a mobile bot to systematically capture thermal infrared imagery for two categories of non-heat generating objects (extended and compact) in several different geographic locations. The extended objects consist of objects that extend beyond the thermal camera\u27s field of view, such as brick walls, hedges, picket fences, and wood walls. The compact objects consist of objects that are within the thermal camera\u27s field of view, such as steel poles and trees. We used these large representative data sets to explore the behavior of thermal-physical features generated from the signals emitted by the classes of objects and design our Adaptive Bayesian Classification Model. We demonstrate that our novel classification model not only displays exceptional performance in characterizing non-heat generating outdoor objects in thermal scenes but it also outperforms the traditional KNN and Parzen classifiers

Over the Air Computing for Satellite Networks in 6G

6G and beyond networks will merge communication and computation capabilities in order to adapt to changes. As they will consist of many sensors gathering information from its environment, new schemes for managing these large amounts of data are needed. For this purpose, we review Over the Air (OTA) computing in the context of estimation and detection. For distributed scenarios, such as a Wireless Sensor Network, it has been proven that a separation theorem does not necessarily hold, whereas analog schemes may outperform digital designs. We outline existing gaps in the literature, evincing that current state of the art requires a theoretical framework based on analog and hybrid digital-analog schemes that will boost the evolution of OTA computing. Furthermore, we motivate the development of 3D networks based on OTA schemes, where satellites function as sensors. We discuss its integration within the satellite segment, delineate current challenges and present a variety of use cases that benefit from OTA computing in 3D networks.Comment: Paper accepted in 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON

Over the air computing for satellite networks in 6G

6G and beyond networks will merge communication and computation capabilities in order to adapt to changes. As they will consist of many sensors gathering information from its environment, new schemes for managing these large amounts of data are needed. For this purpose, we review Over the Air (OTA) computing in the context of estimation and detection. For distributed scenarios, such as a Wireless Sensor Network, it has been proven that a separation theorem does not necessarily hold, whereas analog schemes may outperform digital designs. We outline existing gaps in the literature, evincing that current state of the art requires a theoretical framework based on analog and hybrid digital-analog schemes that will boost the evolution of OTA computing. Furthermore, we motivate the development of 3D networks based on OTA schemes, where satellites function as sensors. We discuss its integration within the satellite segment, delineate current challenges and present a variety of use cases that benefit from OTA computing in 3D networks.This work has received funding by the Spanish ministry of science and innovation under project IRENE (PID2020-115323RB-C31) funded by MCIN/AEI/10.13039/501100011033.Peer ReviewedPostprint (author's final draft