Interesting spatiotemporal rules discovery: application to remotely sensed image databases

International audiencePurpose 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 improve the detection of the interesting rules. Based on a CBR process, it, also, offers three main advantages to users in a remote sensing field: automatism, integration of the users' expectations and combination of several interestingness measures while taking into account the reliability of each one. The performance of the proposed approach is evaluated and compared to other approaches using several real-world datasets. Originality/value This study reports a valuable decision support tool for engineers, environmental authority and personnel who want to identify relevant discovered rules. The resulting rules are useful for many fields such as: disaster prevention and monitoring, growth volume and crops on farm or grassland, planting status of agricultural products, and tree distribution of forests

Towards a multi-approach system for uncertain spatio-temporal knowledge discovery in satellite imagery

International audienceExploiting images coming from different sensors is an important challenge in the remote sensing field. Integration of new knowledge is crucial to help the user interpret satellite images and track their spatio-temporal changes over time. Thus, we propose a new approach to process multi-date satellite images. Our approach combines knowledge discovery from satellite image databases, and fusion methods in order to find out new and relevant knowledge useful to create decision making and prevision models. The choice of the proposed architecture is motivated by two reasons. First, we need to process imperfection related to the knowledge discovery and interpretation processes. Second, we should integrate new, valid, potentially useful and ultimately understandable knowledge hidden in databases. Our work is based on three concepts (multi-agent systems, case based reasoning and rule based reasoning) and is validated through the use of two optical satellite images coming from Landsat 7 representing the region of Matmata (South of Tunisia

A data mining based approach to predict spatiotemporal changes in satellite images

International audienceThe interpretation of remotely sensed images in a spatiotemporal context is becoming a valuable research topic. However, the constant growth of data volume in remote sensing imaging makes reaching conclusions based on collected data a challenging task. Recently, data mining appears to be a promising research field leading to several interesting discoveries in various areas such as marketing, surveillance, fraud detection and scientific discovery. By integrating data mining and image interpretation techniques, accurate and relevant information (i.e. functional relation between observed parcels and a set of informational contents) can be automatically elicited.This study presents a new approach to predict spatiotemporal changes in satellite image databases. The proposed method exploits fuzzy sets and data mining concepts to build predictions and decisions for several remote sensing fields. It takes into account imperfections related to the spatiotemporal mining process in order to provide more accurate and reliable information about land cover changes in satellite images. The proposed approach is validated using SPOT images representing the Saint-Denis region, capital of Reunion Island. Results show good performances of the proposed framework in predicting change for the urban zone

Spatio-temporal modeling for knowledge discovery in satellite image databases

International audienceKnowledge discovery from satellite images in spatio-temporal context remains one of the major challenges in the remote sensing field. It is, always, difficult for a user to manually extract useful information especially when processing a large collection of satellite images. Thus, we need to use automatic knowledge discovery in order to develop intelligent image interpretation systems. In this paper, we present a high-level approach for modeling spatio-temporal knowledge from satellite images. We also propose to use a multi-approach segmentation involving several segmentation methods which help improving images modeling and interpretation. The experiments, made on LANDSAT scenes, show that our approach outperforms classical methods in image segmentation and are able to predict spatio-temporal changes of satellite images