Target Detection: Remote Sensing Techniques for Defence Applications

The tremendous development in remote sensing technology in the recent past has opened up new challenges in defence applications. On important area of such applications is in target detection. This paper describes both classical and newly developed approaches to detect the targets by using remotely-sensed digital images. The classical approach includes statistical classification methods and image processing techniques. The new approach deals with a relatively new sensor technology, namely, synthetic aperture radar (SAR) systems and fast developing tools, like neural networks and multisource data integration for analysis and interpretation. With SAR images, it is possible to detect targets or features of a target that is otherwise not possible. Neural networks and multisource data integration tools also have a great potential in analysing and interpreting remote sensing data for target detection

ARKTOS: An Intelligent System for Satellite Sea Ice Image Analysis

We present an intelligent system for satellite sea ice image analysis named ARKTOS (Advanced Reasoning using Knowledge for Typing Of Sea ice). The underlying methodology of ARKTOS is to perform fully automated analysis of sea ice images by mimicking the reasoning process of sea ice experts and photo-interpreters. Hence, our approach is feature-based, rule-based classification supported by multisource data fusion and knowledge bases. A feature can be an ice floe, for example. ARKTOS computes a host of descriptors for that feature and then applies expert rules to classify the floe into one of several ice classes. ARKTOS also incorporates information derived from other sources, fusing different data towards more accurate classification. This modular, flexible, and extensible approach allows ARKTOS be refined and evaluated by expert users. As a software package, ARKTOS comprises components in image processing, rule-based classification, multisource data fusion, and GUI-based knowledge engineering and modification. As a research project over the past 10 years, ARKTOS has undergone phases such as knowledge acquisition, prototyping, refinement, evaluation and deployment, and finally operationalization at the National Ice Center (NIC). In this paper, we will focus on the methodology of ARKTOS

Data supply chain (DSC): Research synthesis and future directions

In the digital economy, the volume, variety and availability of data produced in myriad forms from a diversity of sources has become an important resource for competitive advantage, innovation opportunity as well as source of new management challenges. Building on the theoretical and empirical foundations of the traditional manufacturing Supply Chain (SC), which describes the flow of physical artefacts as raw materials through to consumption, we propose the Data Supply Chain (DSC) along which data are the primary artefact flowing. The purpose of this paper is to outline the characteristics and bring conceptual distinctiveness to the context around DSC as well as to explore the associated and emergent management challenges and innovation opportunities. To achieve this, we adopt the systematic review methodology drawing on the operations management and supply chain literature and, in particular, taking a framework synthetic approach which allows us to build the DSC concept from the preexisting SC template. We conclude the paper by developing a set of propositions and outlining an agenda for future research that the DSC concept implies

Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements

This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics

Geovisual Analytics Environment for Supporting the Resilience of Maritime Surveillance System

International audienceThis paper presents an original approach for supporting the resilience in Maritime Domain Awareness, based on geovisual analytics. While many research projects focus on developing rules for detecting anomalies at by automated means, there is no support to visual exploration led by human operators. We investigate the use of visual methods for analyzing mobility data of ships. Behaviors of interest can be known (modeled) or unknown, asking for various ways of visualizing and studying the information. We assume that supporting the use of geovisual analytics will make the exploration and the analysis process easier, reducing the cognitive load of the tasks led by the actors of maritime surveillance. The detection and the identification of threats at sea are improved by using adequate visualization methods, regarding the context of use. Our suggested framework is based on ontologies for maritime domain awareness and geovisual analytics environments, coupled to rules

Aggregated search: a new information retrieval paradigm

International audienceTraditional search engines return ranked lists of search results. It is up to the user to scroll this list, scan within different documents and assemble information that fulfill his/her information need. Aggregated search represents a new class of approaches where the information is not only retrieved but also assembled. This is the current evolution in Web search, where diverse content (images, videos, ...) and relational content (similar entities, features) are included in search results. In this survey, we propose a simple analysis framework for aggregated search and an overview of existing work. We start with related work in related domains such as federated search, natural language generation and question answering. Then we focus on more recent trends namely cross vertical aggregated search and relational aggregated search which are already present in current Web search