Wadii Boulila

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

Abstract The 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.

An Intelligent Possibilistic Approach to Reduce the Effect of the Imperfection Propagation on Land Cover Change Prediction

The interpretation of satellite images in a spatiotemporal context is a challenging subject for remote sensing community. It helps predicting to make knowledge driven decisions. However, the process of land cover change (LCC) prediction is generally marred by imperfections which affect the reliability of decision about these changes. Propagation of imperfection helps improve the change prediction process and decrease the associated imperfections. In this paper, an imperfection propagation methodology of input parameters for LCC prediction model is presented based on possibility theory. The possibility theory has the ability to handle both aleatory and epistemic imperfection. The proposed approach is divided into three main steps: 1) an imperfection propagation step based on possibility theory is used to propagate the parameters imperfection, 2) a knowledge base based on machine learning algorithm is build to identify the reduction factors of all imperfection sources, and 3) a global sensitivity analysis step based on Sobol’s method is then used to find the most important imperfection sources of parameters. Compared with probability theory, the possibility theory for imperfection propagation is advantageous in reducing the error of LCC prediction of the regions of the Reunion Island. The results show that the proposed approach is an efficient method due to its adequate degree of accuracy.

Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

This paper presents an approach for reducing uncertainty related to the process of land-cover change (LCC) prediction. LCC prediction models have, almost, two sources of uncertainty which are the uncertainty related to model parameters and the uncertainty related to model structure. These uncertainties have a big impact on decisions of the prediction model. To deal with these problems, the proposed approach is divided into three main steps: (1) an uncertainty propagation step based on possibility theory is used as a tool to evaluate the performance of the model; (2) a sensitivity analysis step based on Hartley-like measure is then used to find the most important sources of uncertainty; and (3) a knowledge base based on machine learning algorithm is built to identify the reduction factors of all uncertainty sources of parameters and to reshape their values to reduce in a significant way the uncertainty about future changes of land cover. In this study, the present and future growths of two case studies were anticipated using multi-temporal Spot-4 and Landsat satellite images. These data are used for the preparation of prediction map of year 2025. The results show that our approach based on possibility theory has a potential for reducing uncertainty in LCC prediction modeling.

An Approach for Imperfection Propagation: Application to Land Cover Change Prediction

We propose an approach that propagates imperfection throu- [2]ghout a model of land cover change prediction. The proposed approach is based on Polynomial Collocation method. The proposed approach estimates the imperfection in the output of the prediction model from the imperfection in its inputs. It incorporates two steps:

Using evidence theory in land cover change prediction to model imperfection propagation with correlated inputs parameters

The identification and the propagation of imperfection are important. In general, imperfection in land cover change (LCC) prediction process can be categorized as both aleatory and epistemic. This imperfection, which can be subdivided into parameter and structural model imperfection, is recognized to have an important impact on results. On the other hand, correlation of input system parameters is often neglected when modeling this system. However, correlation of parameters often blurs the model imperfection and makes it difficult to determine parameter imperfection. Several studies in literature depicts that evidence theory can be applied to model aleatory and epistemic imperfection and to solve multidimensional problems, with consideration of the correlation among parameters. The effective contribution of this paper is to propagate the imperfection associated with both correlated input parameters and LCC prediction model itself using the evidence theory. The proposed approach is divided into two main steps: 1) imperfection identification step is used to identify the types of imperfection (aleatory and/or epistemic), the sources of imperfections, and the correlations of the uncertain input parameters and the used LCC prediction model, and 2) imperfection propagation step is used to propagate aleatory and epistemic imperfection of correlated input parameters and model structure using the evidence theory. The results show the importance to propagate both parameter and model structure imperfection and to consider correlation among input parameters in LCC prediction model. In this study, the changes prediction of land cover in Saint-Denis City, Reunion Island of next 5 years (2016) was anticipated using multi-temporal Spot-4 satellite images in 2006 and 2011. Results show good performances of the proposed approach in improving prediction of the LCC of the Saint-Denis City on Reunion Island.

Reducing uncertainties in land cover change models using sensitivity analysis

Land cover change (LCC) models aim to track spatiotemporal changes made in land cover. In most cases, LCC models contain uncertainties in their main components (i.e., input parameters and model structure). These uncertainties propagate through the modeling system, which generates uncertainties in the model outputs. The aim of this manuscript is to propose an approach to reduce uncertainty of LCC prediction models. The main objective of the proposed approach is to apply a sensitivity analysis method, based on belief function theory, to determine parameters and structures that have a high contribution in the variability of the predictions of the LCC model. Our approach is applied to four common LCC models (i.e., DINAMICA, SLEUTH, CA-MARKOV, and LCM). Results show that uncertainty of the model parameters and structure has meaningful impacts on the final decisions of LCC models. Ignoring this uncertainty can lead to erroneous decision about land changes. Therefore, the presented approach is very useful to identify the most relevant uncertainty sources that need to be processed to improve the accuracy of LCC models. The applicability and effectiveness of the proposed approach are demonstrated through a case study based on the Cairo region. Results show that 13% of the agriculture and 3.8% of the desert lands in 2014 would be converted to urban areas in 2025.

Sensitivity analysis of land cover change prediction model in the presence of aleatory and epistemic imperfection

Making accurate decision about forthcoming Land Cover Change (LCC) are generally complex. Besides, input parameters for LCC prediction systems are varied and married by imperfection that have a significant influence on out results of these systems. This imperfection is divided into two classes: aleatory imperfection and epistemic imperfection. Studying the effect of these parameters on systems output can help improving decision. Sensitivity Analysis (SA) has an important role in the identification and reduction of the imperfection. In literature, Sobol indices, are most popular. However, they have computational cost and time demanding. Recently, the Derivative-based Global Sensitivity Measure (DGSM) appears to overcome this problem. In this paper, we present a SA approach to address both types of imperfections related to LCC prediction model taking into account correlation among parameters. Performances of the proposed approach are proved using several real-world data sets representing the Port region, Reunion Island. Experiments made demonstrate the effectiveness and the efficiency of the proposed approach.

Big Data: Concepts, Challenges and Applications

Big data is an evolving term that describes any voluminous amount of structured, semi-structured and unstructured data that has the potential to be mined for information.Big Data it’s varied, it’s growing, it’s moving fast, and it’s very much in need of smart management In this paper, we first review a literature survey of big data, second review the related technologies, such as could computinq and Hadoop, We then focus on the five phases of the value chain of big data, i.e.,data sources, data collection, data management, data analysis and data visualization. And we finally examine the several representative applications of big data.

Parameter and structural model imperfection propagation using evidence theory in land cover change prediction

To be robust, decision-making process must take account the imperfection associated with models. The identification, understanding and propagation of imperfection sources are important. In general, the imperfection in land cover change (LCC) prediction process can be categorized as both aleatory and epistemic. This imperfection, which can be subdivided into parameter and structural model imperfection, is recognized to have an important impact on actual results. Previously, it has been shown that evidence theory can be applied to model aleatory and epistemic imperfection. The objective of this study is to introduce an efficient methodology for the propagation of imperfection using evidence theory in LCC prediction model, which include both parameter and structural model imperfection sources.

MULTI-APPROACH SATELLITE IMAGES FUSION BASED ON BLIND SOURCES SEPARATION

The development of satellite image acquisition tools helped improving the extraction of information about natural scenes. In the proposed approach, we try to minimize imperfections accompanying the image interpretation process and to maximize useful information extracted from these images through the use of blind source separation (BSS) and fusion methods. In order to extract maximum information from multi-sensor images, we propose to use three algorithms of BSS that are FAST- ICA2D, JADE2D, and SOBI2D. Then by employing various fusion methods such as the probability, possibility, and evidence methods we can minimize both imprecision and uncertainty. In this paper, we propose a hybrid approach based on five main steps. The first step is to apply the three BSS algorithms to the satellites images; it results in obtaining a set of image sources representing each a facet of the land cover. A second step is to choose the image having the maximum of kurtosis and negentropy. After the BSS evaluation, we proceed to the training step using neural networks. The goal of this step is to provide learning regions which are useful for the fusion step. The next step consists in choosing the best adapted fusion method for the selected source images through a case-based reasoning (CBR) module. If the CBR module does not contain a case similar to the one we are seeking, we proceed to apply the three fusion methods. The evaluation of fusion methods is a necessary step for the learning process of our CBR.