Imed Riadh Farah

Interesting spatiotemporal rules discovery: application to remotely sensed image databases

Purpose – Knowledge discovery in databases aims to discover useful and significant information from multiple databases. However, in the remote sensing field, the large size of discovered information makes it hard to manually look for interesting information quickly and easily. The purpose of this paper is to automate the process of identifying interesting spatiotemporal knowledge (expressed as rules).Design/methodology/approach – The proposed approach is based on case‐based reasoning (CBR) process. CBR allows the recognition of useful and interesting rules by simulating a human reasoning process, and combining objective and subjective interestingness measures. It takes advantage of statistics power from objective criteria and the reliability of subjective criteria. This helps improve the discovery of interesting rules by taking into consideration the different properties of interestingness measures.Findings – The proposed approach combines several interestingness measures with complementary properties to...

Multi-agent system for detecting and analysing changes on satellite image sequence

The analysis of satellite images representing natural scenes introduced a very important volume information and requires a substantial elaboration at all levels: pre-processing, segmentation, recognition and interpretation. The consideration of all these phases influences unmistakably the quality of the tasks of processing and time execution. Quality and time are often the only criteria retained by these applications. In this paper, we propose a multi-agent approach which consists in developing a multi-agent system based on parallel processing concept for satellite image analysis changes, we describe the basics of our multi agent architecture concept and show how it can be used as basis of an automatic task parallel processing to speed up image processing. We further illustrate the design and implementation of the multi agent system that uses image analysis agents for generating and processing parallel programs by calling the available method. We validated our approach on satellite images of SPOT4 representing a north Tunisian region for different dates. The results obtained consists in classifying images and detecting the change.

Mining Spatio-Temporal Metadata for Satellite Images Interpretation

Mining the growing data issued from the interpretation of remotely sensed images to obtain the necessary information for land cover change studies becomes more difficult and makes the data volume problem particularly acute. Mitigating this problem requires using data efficiently as metadata for mining and selecting appropriate data for change studies. In this paper, we propose an integrate hierarchical approach based on the use of a blackboard architecture and multi-agent system and having a reasoning ability to find the best strategy to extract and create metadata about extracted objects. This architecture models relation-ship between objects and primitives extracted from images as metadata and use a transition diagram to handle temporal dependencies and perform the detection of temporal changes of objects. We validate our approach on a set of multi-temporal Spot images, to model the evolution of detected object.

Towards a new ontology matching approach based on multi-criteria analysis methods

Actually, we still have not a well established satellite image ontology that would be very useful to assist us to study major facts affecting earth, detect and monitor natural phenomena. Nevertheless, there are several domain-specific geographic ontologies that can be used as semantic resources to build such an ontology. Thus, we can start from a core satellite image ontology such as the one of Durand and enrich it using these geographic ontologies. Ontology matching is one of the principal activities in the ontology enrichment process and highly depends on the similarity measures that are considered as well as the way they are combined together in order to decide whether two concepts coming from different ontologies are alienable. In the literature, research on similarity mainly focuses on issues related to how to compute and refine similarity measures. However, few research addresses studying their dependencies and contributions in the evaluation of the overall similarity between objects to be compared. In this paper and in order to align an initial remote sensing satellite image ontology with a set of geographic ontologies, we give insights on the main similarity models as well as their associated measures. We then propose a method in order to select a reduced set of the most important similarity measures to use for the alignment. Afterwards, we present a method that can produce a ranking model that allows sorting mappings between concepts coming from two different ontologies, in a decreasing order of a global similarity score. First experimentations show that the proposed approach is promising.

A robust Evidential Fisher Discriminant for multi-temporal hyperspectral images classification

This paper develops a noise-robust processing method which can be used to enhance the classification of remotely sensed hyperspectral images. The method first illustrates the benefit of boosting the classical classifiers by exploiting the capability of belief functions. The evidential approach is adopted to produce a map which is approximately insensitive to the noise accompanying the original hyperspectral data-set. Then, a new Evidential Kernel Fisher Discriminant is proposed by using a modified version of the Expectation-Maximization (EM) algorithm. An experimental comparison of the proposed approach with other classical methods is conducted using both synthetic and real hyperspectral data collected by the HYPERION sensor. Our experiments reveal that both classification and unmixing process can benefit from the proposed aggregated approach, remarkably, when the noise level present in the original hyperspectral series is propositionally high.

A methodology for modelling and retrieving satellite images basing on spatial knowledge: application to natural risks

In this paper, we present a methodology for modelling and retrieving satellite images basing on their spatial knowledge. The main idea of our approach is that the use of spatial knowledge, reasoning and inference technique, can contribute to deduce the susceptibility of the scene at natural risks (erosion, flooding, fires, etc.). Our methodology takes in input a set of multi-sensor images representing a scene. It contains four modules: (1) Modelling of the scene, (2) fusion of image annotations, (3) similar case retrieval, and (4) reasoning and interpretation.

A novel decision support system for the interpretation of remote sensing big data

Applications of remote sensing (RS) data cover several fields such as: cartography, surveillance, land-use planning, archaeology, environmental studies, resources management, etc. However, the amount of RS data has grown considerably due to the increase of aerial and satellite sensors. With this continuous increase, the necessity of having automated tools for the interpretation and analysis of RS big data is clearly obvious. The manual interpretation becomes a time consuming and expensive task. In this paper, a novel tool for interpreting and analyzing RS big data is described. The proposed system allows knowledge gathering for decision support in RS fields. It helps users easily make decisions in many fields related to RS by providing descriptive, predictive and prescriptive analytics. The paper outlines the design and development of a framework based on three steps: RS data acquisition, modeling, and analysis & interpretation. The performance of the proposed system has been demonstrated through three models: clustering, decision tree and association rules. Results show that the proposed tool can provide efficient decision support (descriptive and predictive) which can be adapted to several RS users’ requests. Additionally, assessing these results show good performances of the developed tool.