Imed Riadh Farah

Imed Riadh Farah
Université de la Manouba | ESCT · Laboratoire RIADI-GDL

Professor

About

276
Publications
35,239
Reads
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1,946
Citations
Citations since 2016
153 Research Items
1667 Citations
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500
Additional affiliations
January 2009 - February 2016
Université de la Manouba
Position
  • Professor (Associate)
January 1993 - present
Ecole Nationale des Sciences de l'Informatique
Position
  • Researcher
January 1993 - present
Ecole Nationale des Sciences de l'Informatique
Position
  • Researcher

Publications

Publications (276)
Article
The multi-resolution analysis based on wavelet transform (WT) has widely proved its effectiveness for the analysis of non-stationary time series (TS) in the remote sensing field. However, there are three essential parameters affecting wavelet-aided data in vegetation dynamics detection. This study proposes a new methodology that serves for the prop...
Book
Full-text available
Call for Book Chapters – Free Publication in Springer For more information, visit: https://www.riotu-lab.org/dmsrm/ DUE DATES Full-length paper submission: June 15, 2022 Notification of first decision: July 31, 2022 Revised chapter submission: September 30, 2022 Final decision: October 31, 2022 Camera-ready: November 30, 2022
Article
Earth Observation (EO) technologies have played an increasingly important role in monitoring the Sustainable Development Goals (SDG). These technologies often combined with Machine Learning (ML) models provide efficient means for achieving the SDGs.The great progress of this combination is also demonstrated by the large number of software, web tool...
Article
The rapid increase in the number of Earth Observation (EO) systems generates a massive amount of heterogeneous data. It has raised big issues in collecting, preprocessing, storing, and the visualization these data. However, traditional techniques are facing serious challenges when dealing with big EO data dimensions (i.e., Volume, Veracity, Variety...
Article
In this letter, we aim to reveal the most accurate method for soil moisture (SM) retrieving. Based on the synergistic use of Sentinel-1 (S-1) and Sentinel-2 (S-2), the most popular Machine Learning (ML) techniques in addition to the Water Cloud Model (WCM) are evaluated. Experiments were carried out at two different sites Dar Dhaoui and Chammakh, l...
Article
Full-text available
Over the past few years, total financial investment in the agricultural sector has increased substantially. Palm tree is important for many countries’ economies, particularly in northern Africa and the Middle East. Monitoring in terms of detection and counting palm trees provides useful information for a variety of stakeholders; it helps in yield e...
Chapter
During the analysis and management of flood events, the issue of time is crucial. In fact, such events require accurate knowledge of the time of their occurrence and their temporal relationships. However, in most cases, temporal information about flood events are uncertain. In this paper, we propose an intelligent system for managing the temporal u...
Article
Full-text available
Deep learning (DL) has shown outstanding performances in many fields, including remote sensing (RS). DL is turning into an essential tool for the RS research community. Recently, many cloud platforms have been developed to provide access to large-scale computing capacity, consequently permitting the usage of DL architectures as a service. However,...
Preprint
Full-text available
Over the past few years, total financial investment in the agricultural sector has increased substantially. Palm tree is important for many countries' economies, particularly in northern Africa and the Middle East. Monitoring in terms of detection and counting palm trees provides useful information for various stakeholders; it helps in yield estima...
Conference Paper
Soil moisture (SM) retrieving has received a lot of attention lately due to its essential role in agricultural activity, disaster management such as drought, and ensuring food security. The aim of this article is to investigate the performance of inverted backscattering models with different Machine Learning (ML) methods in an arid region, in the s...
Conference Paper
The investigation of climatic data has an important significance in the understanding and the vegetation dynamics. In fact, Vegetation change analysis lies at the crossroad of two U.N. 2030 Sustainable Development Goals (SDGs): Life and Land (SDG15) and climate actions (SDG13). Currently, a large number of deep learning algorithms have been develop...
Conference Paper
The Sustainable Development Goals (SDGs) have great implications for country-wide improvement and making plans in both developed and developing nations in the post-2015 period to 2030. The SDGs are a set of 17 interlinked global goals designed to be a plan to attain a better and extra sustainable future for all, especially with the synergistic comb...
Conference Paper
Desertification risk detection is a challenging problem because of the dynamic climate change and human activity. This paper proposes a methodology to detect regions with high risk of desertification based on Landsat imagery (RGB bands and NDVI) and variational auto-Encoder (VAE). The considered features (RGB and NDVI) are extracted from multitempo...
Article
In our world of rapid change, floods are a growing threat. In this context, flood extent mapping is important for damage and accurate and timely information about flood-affected areas are needed. Due to the suitability of remote sensing in mapping flood inundation, multi-source satellite images have been used in recent years. This paper presents a...
Conference Paper
Full-text available
This paper explores Deep Learning (DL) method to classify Motor Imagery (MI) based on electroencephalogram (EEG) brainwaves data using the two feature extractors Independent Component Analysis (ICA) and Discrete Wavelet Transform (DWT). The contribution of this paper is in the processing phase, more precisely in the intermediate level by applying t...
Conference Paper
Full-text available
Un grand nombre de capteurs d'observation de la Terre génèrent quotidiennement uneénorme quantité de données (des données climatiques, des données satellitaires, des données hydrologiques, etc.). Ces données sont utilisées dans différents domaines tels que la surveillance/prévision de la sécheresse. La sécheresse est considérée comme le risque natu...
Conference Paper
Full-text available
Remote sensing imagery classification has seen various problems such as the existence of mixed pixels. In fact, sub-pixel mapping proved recently to be an effective solution for this problem, whereas it provides a fine-resolution map of class labels from across spectrally unmixed fraction images. To improve sub-pixel mapping precision, we propose a...
Conference Paper
Full-text available
La télédétection spatiale représente un moyen efficace pour parvenir à révéler la présence de failles ou de fractures. Les images satellites deviennent de plus en plus accessibles en particulier celles de Sentinel-1A et Sentinel-2A dont l'ensemble de leurs données est mis a la disposition du grand public avec un accès total et gratuit. La première...
Conference Paper
Full-text available
Les requêtes adresséesà des bases de données temporelles sont souvent exprimées de manière graduelle et imprécise. Cette incertitude et cette imprécision des requêtes tem-porelles posent un problème dans le contexte des systèmes de questions/réponses. Dans cet article, nous présentons une approche intelligente pour traiter les requêtes temporelles...
Conference Paper
Full-text available
Les données peuvent être un ensemble de caractères, de nombres ou même de symboles appartenant à différents phénomènes de la nature observés par diverses techniques. Un ensemble de données mises dans un contexte spécifique, collectées et enregistrées avec un certain pas de temps, mieux connu sous le nom de séries temporelles sur lesquelles certaine...
Conference Paper
Full-text available
This study aims to improve the automatic detection of epileptic seizures (ESs) using machine learning (ML) algorithms applied to electroencephalography (EEG) brainwave data. Previous studies based on a database published online showed high seizure detection accuracies, but they contrasted seizure activity to all kinds of non-seizure EEG activity, r...
Article
Soil Moisture (SM) is an important parameter used to control a broad range of environmental applications. An increasing attention has been recently given to Machine Learning (ML) methods for SM retrieval, that provide promising perfor- mance. Nevertheless, most of them are based on a supervised learning strategies, that depend on the used labeled t...
Article
Active remotely sensed data can be used to perform a variety of forestry tasks including stand characterization, inventory, and management of forest and fire behavior modeling. The present work investigates the potential of Airborne Laser Scanning (ALS) derived methods applied in the deciduous forest by processing an individual tree detection (ITD)...
Article
Objectives Feature selection in data sets is an important task allowing to alleviate various machine learning and data mining issues. The main objectives of a feature selection method consist on building simpler and more understandable classifier models in order to improve the data mining and processing performances. Therefore, a comparative evalua...
Chapter
In this paper, a suitable method to forecast the normalized difference vegetation index (NDVI) time series (TS) is deep learning in the context of remote sensing big data. In fact, we proposed a non-stationary NDVI time series forecasting model by combining big data system, wavelet transform (WT), and long short-term memory (LSTM) neural network. I...
Chapter
The aim of this paper was to propose a long short-term memory model (LSTM) for drought forecasting in Iran based on the standardized precipitation evapotranspiration index (SPEI). The model results were compared with three different machine learning methods. The same input data were used, including rainfall, temperature (mean, minimum, and maximum)...
Article
The availability of long-term time series (TS) derived from remote sensing (RS) images is favorable for the analysis of vegetation variation and dynamics. However, the choice of appropriate methods is a challenging task. This article presented an experimental comparison of four methods widely used for the detection of long-term trend and seasonal c...
Article
Monitoring environmental evolutions, one of the most crucial axes on which sustainable development is based, requires the knowledge of information on the observed geographical scenes. Due to the continuous technological developments in the remote sensing field, the data sources increase exponentially over time, and the information contained in sate...
Article
Over the last few years, Deep learning (DL) approaches have been shown to outperform state-of-the-art machine learning (ML) techniques in many applications such as vegetation forecasting, sales forecast, weather conditions, crop yield prediction, landslides detection and even COVID-19 spread predictions. Several DL algorithms have been employed to...
Article
Full-text available
Recently, remotely sensed data obtained via laser technology has gained great importance due to its wide use in several fields, especially in 3D urban modeling. In fact, 3D city models in urban environments are efficiently employed in many fields, such as military operations, emergency management, building and height mapping, cadastral data upgradi...
Article
In this letter, we propose a new methodology for Satellite Image Time Series (SITS) land cover mapping, named Two Branches Convolutional Neural Network (TwoBCNN). The main objective of the proposed methodology is to combine pixel- and object-level multi-variate time-series information in the classification process. Experiments were carried out on a...
Article
The amount of remote sensing (RS) data has increased at an unexpected scale, due to the rapid progress of earth-observation and the growth of satellite RS and sensor technologies. Traditional relational databases attend their limit to meet the needs of high-resolution and large-scale RS Big Data management. As a result, massive RS data management i...
Article
Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. However, it has been proven that developing efficient methods for solving CSP, especially for large problems, is very difficult and challenging. Existing complete methods for problem-solving are in most cases unsuitable...
Chapter
This paper proposes a new hybrid approach I-WT-LSTM (i.e., Improved Wavelet Long Short-Term Memory (LSTM) Model) for forecasting non-stationary time series (TS) from satellite imagery. The proposed approach consists of two steps: The first step aims at decomposing TS using Multi-Resolution Analysis wavelet (MRA-WT) into inter-and intra-annual compo...
Conference Paper
The increasing amount of data generated by earth observation missions like Copernicus, NASA Earth Data, and climate stations is overwhelming. Every day, terabytes of data are collected from these resources for different environment applications. Thus, this massive amount of data should be effectively managed and processed to support decision-makers...
Article
Full-text available
The aim of this paper is to improve trend analysis for non-stationary Normalized Difference Vegetation Index (NDVI) time series (TS) over different areas in Tunisia based on the wavelet transform (WT) multi-resolution analysis (MRA-WT), statistical test, and meteorological data. The MRA-WT was applied in order to decompose the TS into different com...
Article
Different studies on predicting future green cover changes exist with various success levels. Each one focuses on a different story about which model is more appropriate. Therefore, finding a suitable model remains a difficult task due to the remotely sensed data issues and the complexity of the vegetation cover change process. Despite the unicity...
Preprint
Full-text available
Constraint satisfaction problem (CSP) has been actively used for modeling and solving a wide range of complex real-world problems. However, it has been proven that developing efficient methods for solving CSP, especially for large problems, is very difficult and challenging. Existing complete methods for problem-solving are in most cases unsuitable...
Preprint
Full-text available
The amount of remote sensing (RS) data has increased at an unexpected scale, due to the rapid progress of earth-observation and the growth of satellite RS and sensor technologies. Traditional relational databases attend their limit to meet the needs of high-resolution and large-scale RS Big Data management. As a result, massive RS data management i...
Article
Full-text available
Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand so...
Chapter
In this paper, the trends in non-stationary Normalized Difference Vegetation Index (NDVI) Time Series (TS) over different areas in Tunisia are analyzed by applying wavelet transform and statistical tests. In the first step, the Discrete Wavelet Transform (DWT) was applied on three different time series in order to detect changes. Therefore, the dif...
Article
Full-text available
In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their pre...
Chapter
To date, analysis of remotely sensed images remains a big challenge. Despite its high quality and free availability, scientists ask more questions about the reliability of the existent works and developed tools. Indeed, the input choice is under investigation in order to minimize the imprecision within the work's methodology and results. In order t...
Conference Paper
Full-text available
This article attempts to provide an overview of the basic concepts, structure and terms related to case-based reasoning, because we aim to work on an evolution of the CBR process which allows us to integrate the knowledge base, ensure the consistency of information, integrate the modes of reasoning: similarity, classification and hybrid. In additio...
Conference Paper
Big Data is an emerging field since massive storage and computing capabilities have been made available by sophisticated infrastructures. Drought studies are likely to benefit from Big Data techniques supporting the processing of a large number of heterogeneous data. In fact, an early detection, accurate drought evalu-ation, and efficient predictio...
Article
This paper reviews big data and Internet of Things (IoT)-based applications in smart environments. The aim is to identify key areas of application, current trends, data architectures, and ongoing challenges in these fields. To the best of our knowledge, this is a first systematic review of its kind, that reviews academic documents published in peer...