Application of Multi-Sensor Fusion Technology in Target Detection and Recognition

Application of multi-sensor fusion technology has drawn a lot of industrial and academic interest in recent years. The multi-sensor fusion methods are widely used in many applications, such as autonomous systems, remote sensing, video surveillance, and the military. These methods can obtain the complementary properties of targets by considering multiple sensors. On the other hand, they can achieve a detailed environment description and accurate detection of interest targets based on the information from different sensors.This book collects novel developments in the field of multi-sensor, multi-source, and multi-process information fusion. Articles are expected to emphasize one or more of the three facets: architectures, algorithms, and applications. Published papers dealing with fundamental theoretical analyses, as well as those demonstrating their application to real-world problems

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

DETECTING PHYTOPLANKTON SIZE CLASS USING SATELLITE EARTH OBSERVATION

A new range of multi-plankton biogeochemical models have recently been developed, designed to advance our understanding of the ocean carbon cycle to improve predictions of its future influence on climate. Synoptic measurements of the different phytoplankton communities are required to validate and ultimately improve such models. Measuring ocean colour from satellite is the only method currently available for synoptically monitoring wide-area properties of ocean ecosystems, such as phytoplankton chlorophyll biomass. Recently, a variety of bio-optical methods have been established that use satellite data to identify and differentiate between either phytoplankton functional types (PFTs) or phytoplankton size classes (PSCs). In this thesis, several of these techniques were evaluated against in situ observations (6504 samples) to determine their ability to detect dominant phytoplankton size classes (micro-, nano- and picoplankton). Results show that spectral-response, ecological and abundance-based approaches can all perform with similar accuracy. However, abundance-based approaches provide better spatial retrieval of PSCs. Based on insights into the abundance-based models, and by utilising a large pigment database, a new three-component model was developed which calculates the fractional contributions of three phytoplankton size classes (micro-, nano- and picoplankton) to the overall chlorophyll-a concentration. Using a globally representative, independent, coupled pigment and satellite dataset the model estimates fractional contributions with a mean accuracy of 9.2 % for microplankton, 17.1 % for nanoplankton and 16.1 % for picoplankton. The effect of optical depth on the model parameters was also investigated and explicitly incorporated into the model. Using the three-component model, the two-component absorption model of Sathyendranath et al. (2001) and Devred et al. (2006) was extended to three-component populations of phytoplankton, namely, pico-, nano- and microplankton. The new model infers total and size-dependent phytoplankton absorption as a function of the total chlorophyll-a concentration. A main characteristic of the model is that all the parameters that describe it have biological or optical interpretation. The three-component model performs better than the two-component model, at retrieving total phytoplankton absorption. Accounting for the contribution of pico- and nanoplankton, rather than the combination of both used in the two-component model, improved significantly the retrieval of phytoplankton absorption at low chlorophyll-a concentrations. The three-component model was applied to a decade of ocean colour observations. In the equatorial region of the Pacific and Indian Oceans, phytoplankton size class anomalies (% total chlorophyll-a) were highly correlated with indices of both the El Niño (La Niña) Southern Oscillation and the Indian Ocean Dipole. Furthermore, in these regions, micro- and nanoplankton size class anomalies were negatively correlated with anomalies of the sea surface temperature, sea surface height and stratification. Whereas, the picoplankton size class anomalies were positively correlated with these physical variables. Results from this thesis indicate that phytoplankton size class can be retrieved from Earth Observation with reasonable accuracy. It is recommended that such information can now be assimilated into multi-plankton biogeochemical models, or alternatively, verify them.NER

Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Proceedings of the 10th International Conference on Ecological Informatics: translating ecological data into knowledge and decisions in a rapidly changing world: ICEI 2018

The Conference Proceedings are an impressive display of the current scope of Ecological Informatics. Whilst Data Management, Analysis, Synthesis and Forecasting have been lasting popular themes over the past nine biannual ICEI conferences, ICEI 2018 addresses distinctively novel developments in Data Acquisition enabled by cutting edge in situ and remote sensing technology. The here presented ICEI 2018 abstracts captures well current trends and challenges of Ecological Informatics towards: • regional, continental and global sharing of ecological data, • thorough integration of complementing monitoring technologies including DNA-barcoding, • sophisticated pattern recognition by deep learning, • advanced exploration of valuable information in ‘big data’ by means of machine learning and process modelling, • decision-informing solutions for biodiversity conservation and sustainable ecosystem management in light of global changes

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

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