Earth‐Observation Data Access: A Knowledge Discovery Concept for Payload Ground Segments

In recent years the ability to store large quantities of Earth Observation (EO) satellite images has greatly surpassed the ability to access and meaningfully extract information from it. The state-of-the-art of operational systems for Remote Sensing data access (in particular for images) allows queries by geographical location, time of acquisition or type of sensor. Nevertheless, this information is often less relevant than the content of the scene (e.g. specific scattering properties, structures, objects, etc.). Moreover, the continuous increase in the size of the archives and in the variety and complexity of EO sensors require new methodologies and tools - based on a shared knowledge - for information mining and management, in support of emerging applications (e.g.: change detection, global monitoring, disaster and risk management, image time series, etc.). In addition, the current Payload Ground Segments (PGS) are mainly designed for Long Term Data Preservation (LTDP), in this article we propose an alternative solution for enhancing the access to the data content. Our solution presents a knowledge discovery concept, whose intention is to implement a communication channel between the PGS (EO data sources) and the end-user who receives the content of the data sources coded in an understandable format associated with semantics and ready for the exploitation. The first implemented concepts were presented in Knowledge driven content based Image Information Mining (KIM) and Geospatial Information Retrieval and Indexing (GeoIRIS) system as examples of data mining systems. Our new concept is developed in a modular system composed of the following components 1) the data model generation implementing methods for extracting relevant descriptors (low-level features) of the sources (EO images), analyzing their metadata in order to complement the information, and combining with vector data sources coming from Geographical Information Systems. 2) A database management system, where the database structure supports the knowledge management, feature computation, and visualization tools because of the modules for analysis, indexing, training and retrieval are resolved into the database. 3) Data mining and knowledge discovery tools allowing the end-user to perform advanced queries and to assign semantic annotations to the image content. The low-level features are complemented with semantic annotations giving meaning to the image information. The semantic description is based on semi-supervised learning methods for spatio-temporal and contextual pattern discovery. 4) Scene understanding counting on annotation tools for helping the user to create scenarios using EO images as for example change detection analysis, etc. 5) Visual data mining providing Human-Machine Interfaces for navigating and browsing the archive using 2D or 3D representation. The visualization techniques perform an interactive loop in order to optimize the visual interaction with huge volumes of data of heterogeneous nature and the end-user

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Bag-of-Features Image Indexing and Classification in Microsoft SQL Server Relational Database

This paper presents a novel relational database architecture aimed to visual objects classification and retrieval. The framework is based on the bag-of-features image representation model combined with the Support Vector Machine classification and is integrated in a Microsoft SQL Server database.Comment: 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, 24-26 June 201

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and $K$-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

Image databases: Problems and perspectives

With the increasing number of computer graphics, image processing, and pattern recognition applications, economical storage, efficient representation and manipulation, and powerful and flexible query languages for retrieval of image data are of paramount importance. These and related issues pertinent to image data bases are examined