Saeid Homayouni

Saeid Homayouni
Institut National de la Recherche Scientifique | INRS · Eau Terre Environnement Centre

Ph.D. in Remote Sensing (Signal and Image Processing) Telecom Paris France

About

194
Publications
49,366
Reads
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2,145
Citations
Introduction
Saeid Homayouni is currently an associate professor of environmental remote sensing and geomatics at the Center for Water, Earth, and Environment (Centre Eau Terre Environnement: ETE) of the Institut National de la Recherche Scientifique (INRS) in Quebec, Canada. His research activities are mainly focused on Earth observation analytics (e.g., optical, SAR, etc.) for urban and agro-environmental applications.
Additional affiliations
April 2019 - present
Institut National de la Recherche Scientifique
Position
  • Professor (Associate)
July 2015 - March 2019
University of Ottawa
Position
  • Lecturer
July 2013 - June 2015
University of Ottawa
Position
  • Professor (Assistant)
Education
September 2001 - February 2006
Télécom ParisTech
Field of study
  • Signal and Image

Publications

Publications (194)
Article
Agricultural production monitoring plays a key role in a variety of economic and environmental practices including crop yield forecasting, identifying risk of disease and application of chemicals. Remote sensing has the potential to provide accurate crop condition information across large areas and has the ability to deliver information products in...
Article
One of the most challenging problems in automated clustering of hyperspectral data is determining the number of clusters (NOC) either prior to or during the clustering. We propose a statistical method for best estimating the NOC, not only prior to but also independent of the clustering. This method uses both residual analysis (RA) and change point...
Article
The accurate estimation of the number of endmembers (NOE) in a given hyperspectral imagery plays a fundamental role in the effective classification, clustering, unmixing, and identification of the materials presenting in any remote scene. The optimal estimation of the NOE, however, is a quite challenging task, due to the inevitable combined presenc...
Article
Unsupervised or clustering algorithms can be considered to overcome the need for both high-quantity and high-quality training data for hyperspectral data classification. One of the most widely used algorithms for the clustering of remotely-sensed data is partitional clustering. Partitional clustering is affected by 1) the optimal number of clusters...
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Shallow convolutional neural networks (CNNs) have successfully been used to classify polarimetric synthetic aperture radar (PolSAR) imagery. However, one drawback of the existing deep CNN-based techniques is that the input PolSAR training data are often insufficient due to their need for a significant number of training data compared to shallow CNN...
Article
Sea-land segmentation (SLS) is an essential remote sensing task for various coastal and environmental studies such as coastline extraction, coastal erosion, coastal area monitoring, and ship or iceberg detection. This study aims at improving the SLS performance by modifying the Standard U-Net (SUN) model and developing an automatic coastline extrac...
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Feature selection to reduce redundancies for efficient classification is necessary but usually time consuming and challenging. This paper proposed a comprehensive analysis for optimum feature selection and the most efficient classifier for accurate urban area mapping. To this end, 136 multiscale textural features alongside a panchromatic band were...
Article
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In cold regions, ice jams frequently result in severe flooding due to a rapid rise in water levels upstream of the jam. Sudden floods resulting from ice jams threaten human safety and cause damage to properties and infrastructure. Hence, ice-jam prediction tools can give an early warning to increase response time and minimize the possible damages....
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This study combined Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variable datasets to model leaf area index (LAI) and basal area per ha (BAPH) of two economically important tree species in Northeast, USA; red spruce (Picea rubens Sarg.; RS), and balsam fir (Abies balsamea (L.) Mill.; BF). We used Random Forest (RF),...
Article
Soil moisture is a critical land variable that controls the energy and mass balance in land-atmosphere interactions. Spaceborne Synthetic Aperture Radar (SAR) sensors offer an efficient way to map and monitor soil moisture because of their sensitivity towards the dielectric and geometric properties of the target. In addition, SAR acquisitions are w...
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Active fires are devastating natural disasters that cause socio-economical damage across the globe. The detection and mapping of these disasters require efficient tools, scientific methods, and reliable observations. Satellite images have been widely used for active fire detection (AFD) during the past years due to their nearly global coverage. How...
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Biophysical parameter retrieval using remote sensing has long been utilized for crop yield forecasting and economic practices. Remote sensing can provide information across a large spatial extent and in a timely manner within a season. Plant Area Index (PAI), Vegetation Water Content (VWC), and Wet-Biomass (WB) play a vital role in estimating crop...
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Recent landslide detection studies have focused on pixel-based deep learning (DL) approaches. In contrast, intuitive annotation of landslides from satellite imagery is based on distinct features rather than individual pixels. This study examines the feasibility of the integration framework of a DL model with rule-based object-based image analysis (...
Article
Crop biophysical parameters, such as Leaf Area Index (LAI) and biomass, are essential for estimating crop productivity, yield modeling, and agronomic management. This study used several features extracted from multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) and spectral vegetation indices extracted from Sentinel-2 optical data to estimate...
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Different methods have been proposed in population dynamics to estimate carrying capacity (K). This study estimates K for Iran, using three novel methods by integrating land and water limits into assessments based on Human Appropriated Net Primary Production (HANPP). The first method uses land suitability as the limiting resource. It gives theoreti...
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The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase with the performance evaluation being currently active. This evaluation aims to confirm that the innovative design of the mission’s synthetic aperture radar (SAR) meets the expectations of intended users. In this study, we provide an overview of initial results o...
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Urban sprawl is a universal phenomenon and can be seen as a city’s low-density and haphazard development from the centre to suburban areas, and it has different adverse environmental effects at local and regional scales, including increasing the cost of infrastructure. Geospatial data and technology can be used to measure urban sprawl and predict u...
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This paper proposes a new approach based on an unsupervised deep learning (DL) model for landslide detection. Recently, supervised DL models using convolutional neural networks (CNN) have been widely studied for landslide detection. Even though these models provide robust performance and reliable results, they depend highly on a large labeled datas...
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In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ attention in data science due to their superior resul...
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Global crop mapping and monitoring requires high-resolution spatio-temporal information. In this regard, dual polarimetric Synthetic Aperture Radar (SAR) sensors provide high temporal and high spatial resolutions with large swath width. Generally, crop phenological development studies utilized SAR backscatter intensity-based descriptors. However, t...
Article
Remote Sensing (RS) technology provides regular monitoring of alfalfa farms, as a major source of forage production worldwide. Phenological characteristics derived from time series of RS imagery provide a valuable information source to estimate crop yield accurately. In this study, we computed spectral vegetation indices (SVIs) from time series of...
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We investigated the sensitivity to vegetation cover type of active (PALSAR) and passive (SMAP) freeze/thaw (F/T) classification. We also used F/T classification from high-resolution PALSAR data (30 m) to follow the evolution of frozen and thawed soil states obtained from an adaptive algorithm with low-resolution SMAP data (36 km). We used PALSAR an...
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Oil spills are one of the most hazardous disasters with significant short-and long-term effects on fragile marine ecosystems. Synthetic Aperture Radar (SAR) has been considered an effective technology for mapping and monitoring oil spills in the marine environment, primarily thanks to its weather-, illumination-, and time-independent capabilities....
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In Canada, climate change is expected to increase the extreme precipitation events by magnitude and frequency, leading to more intense and frequent river flooding. In this study, we attempt to map the flood hazard and damage under projected climate scenarios (2050 and 2080). The study was performed in the two most populated municipalities of the Pe...
Conference Paper
Leaf Area Index (LAI) and biomass are the most critical biophysical parameters for crop monitoring. In this study, we used three ensemble-based methods, including Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), for crop parameter estimation and mapping of soybean and wheat in an agricultural region in Winnipeg, Cana...
Article
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Remote sensing data are considered as one of the primary data sources for precise agriculture. Several studies have demonstrated the excellent capability of radar and optical imagery for crop mapping and biophysical parameter estimation. This paper aims at modeling the crop biophysical parameters, e.g., Leaf Area Index (LAI) and biomass, using a co...
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High-resolution urban image clustering has remained a challenging task. This is mainly because its performance strongly depends on the discrimination power of features. Recently, several studies focused on unsupervised learning methods by autoencoders to learn and extract more efficient features for clustering purposes. This paper proposes a Booste...
Article
Monitoring, maintaining, and organizing power lines corridors are of great importance because they are a primary means to transfer generated electricity from power stations to surrounding areas. Mobile Terrestrial Laser Scanning (MTLS) systems have significant potential for efficiently creating a power line infrastructure inventory. In this paper,...
Article
Hitherto there have been many studies comparing the usefulness of OLI and ETM+ sensors for linear feature extraction. However, not too much attention has been paid to the differences in the bandwidth of the two sensors. In this study, the suitability of Landsat ETM+ and OLI sensors for automatic detection of linear features by LINE algorithm was co...
Article
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The water cloud model (WCM) can be inverted to estimate leaf area index (LAI) using the intensity of backscatter from synthetic aperture radar (SAR) sensors. Published studies have demonstrated that the WCM can accurately estimate LAI if the model is effectively calibrated. However, calibration of this model requires access to field measures of LAI...
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Since the rise of deep learning in the past few years, convolutional neural networks (CNNs) have quickly found their place within the remote sensing (RS) community. As a result, they have transitioned away from other machine learning techniques, achieving unprecedented improvements in many specific RS applications. This paper presents a meta-analys...
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The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared (VNIR) bands of WorldView-3 (WV-3) satellite imagery. The study area is Hormuz Island, southern Iran, a salt dome composed of dominant sedimentary and igneous rocks. When performing the...
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Radar penetration in brine-wetted snow-covered sea ice is almost nil, yet reports exist of a correlation between snow depth or ice thickness and SAR parameters. This article presents a description of snow depth and first-year sea ice thickness distributions in three fjords of the Hudson Strait and of their tenuous correlation with SAR backscatterin...
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Crop mapping is a challenging task due to the spatial, spectral, and temporal variations within the cropland. These variations cause high intra-class and low inter-class variability problems. In this study, inspired by Deep Learning (DL) techniques, two Auto-Encoder (AE)-based learning schemes are proposed to exploit the spatio-temporal features in...
Article
Development of the Canadian Wetland Inventory Map (CWIM) has thus far proceeded over two generations, reporting the extent and location of bog, fen, swamp, marsh, and water wetlands across the country with increasing accuracy. Each generation of this training inventory has improved the previous results by including additional reference wetland data...
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Multiplekernel learning (MKL) algorithms are among the most successful classification methods for hyperspectral data. Nevertheless, these algorithms suffer from two main drawbacks of computational complexity and debility to admit to the end-to-end learning paradigm. This article proposed a convolutional kernel classifier (CKC) for hyperspectral rem...
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Digital elevation model (DEM) plays a vital role in hydrological modelling and environmental studies. Many essential layers can be extracted from this land surface information, including slope, aspect, rivers, and curvature. Therefore, DEM quality and accuracy will affect the extracted features and the whole process of modeling. Despite freely avai...
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Wetlands across Canada have been, and continue to be, lost or altered under the influence of both anthropogenic and natural activities. The ability to assess the rate of change to wetland habitats and related spatial pattern dynamics is of importance for effective and meaningful management and protection, particularly under the current context of c...
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This paper presents a new framework for floodplain inundation modeling in an ungauged basin using unmanned aerial vehicles (UAVs) imagery. This method is based on the integrated analysis of high-resolution ortho-images and elevation data produced by the structure from motion(SfM) technology. To this end, the Flood-Level Marks (FLMs) were created fr...
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Unmanned Aerial Vehicle (UAV) imaging systems have recently gained significant attention from researchers and practitioners as a cost-effective means for agro-environmental applications. In particular, machine learning algorithms have been applied to UAV-based remote sensing data for enhancing the UAV capabilities of various applications. This syst...
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Traditional mapping and monitoring of agricultural fields are expensive, laborious, and may contain human errors. Technological advances in platforms and sensors, followed by artificial intelligence (AI) and deep learning (DL) breakthroughs in intelligent data processing, led to improving the remote sensing applications for precision agriculture (P...
Article
Unsupervised feature selection (UFS) is a standard approach to reduce the dimensionality of hyperspectral images (HSIs). The main idea in UFS is to define a similarity metric, and select the features minimizing the metric to reduce the data redundancy. In this paper, we proposed a novel criterion for unsupervised dimensionality reduction based on t...
Article
The dust storm is one of the severe natural disasters that has been recently threatening the Middle East region due to climate changes and human activities. This phenomenon has become a national crisis in some countries in this region in previous years, especially in spring and summer. This research aims to detect and monitor the areas covered by t...
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Recently, there has been a significant increase in efforts to better inventory and manage important ecosystems across Canada using advanced remote sensing techniques. In this study, we improved the method and results of our first-generation Canadian wetland inventory map at 10-m resolution. The main contributions of this new study, as it compares t...
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North America is covered in 2.5 million km 2 of wetlands, which is the remainder of an estimated 56% of wetlands lost since the 1700s. This loss has resulted in a decrease in important habitat and services of great ecological, economic, and recreational benefits to humankind. To better manage these ecosystems, since the 1970s, wetlands in North Ame...
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In this paper, we propose a novel approach based on the active contours model for change detection from synthetic aperture radar (SAR) images. In order to increase the accuracy of the proposed approach, a new operator was introduced to generate a difference image from the before and after change images. Then, a new model of active contours was deve...
Article
Accurate estimation of biomass and Leaf Area Index (LAI) requires appropriate models and predictor variables. These biophysical parameters are indicative of crop productivity, and thus, are of interest in applications such as crop yield forecasting and precision farming. This study evaluated the potential of leveraging vegetation indices derived fr...
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Efforts to use satellites to monitor the condition and productivity of crops, although extensive, can be challenging to operationalize at field scales in part due to low frequency revisit of higher resolution space-based sensors, in the context of an actively growing crop canopy. The presence of clouds and cloud shadows further impedes the exploita...
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Detailed information on the spatial distribution of wetlands is crucial for sustainable management and resource assessment. Furthermore, regularly updated wetland inventories are of particular importance given that wetlands comprise a dynamic, rather than permanent, land condition. Accordingly, satellite-derived wetland maps are greatly beneficial,...
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This paper introduces a novel multi-view multi-learner (MVML) active learning method, in which the different views are generated by a genetic algorithm (GA). The GA-based view generation method attempts to construct diverse, sufficient, and independent views by considering both inter-and intra-view confidences. Hyperspectral data inherently owns hi...
Article
Above ground biomass is an important crop biophysical parameter for monitoring crop condition and determining crop productivity, in particular if linked with phenological growth stage. Optical reflectance and Synthetic Aperture Radar (SAR) backscatter have been used to model above ground biomass for some crops. However, to date, direct comparisons...