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Masoud Mahdianpari

Masoud Mahdianpari
C-CORE and Memorial University of Newfoundland · Electrical Engineering

PhD
Cross-Appointed Professor

About

104
Publications
76,444
Reads
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2,137
Citations
Introduction
Masoud Mahdianpari is currently a Remote Sensing Technical Lead with C-CORE and a Cross-Appointed Professor with the Department of Electrical and Computer Engineering, Memorial University of Newfoundland. His research interests include remote sensing and image analysis, with a special focus on PolSAR image processing, multisensor data classification, machine learning, geo big data, and deep learning.
Additional affiliations
June 2019 - present
Centre for Cold Ocean Resources Engineering (C-CORE)
Position
  • Researcher
September 2015 - May 2019
Centre for Cold Ocean Resources Engineering (C-CORE)
Position
  • Research Assistant
September 2014 - September 2015
Cartographic Center
Position
  • Instructor
Description
  • Course: Digital Image Processing
Education
September 2015 - May 2019
Memorial University of Newfoundland
Field of study
  • Electrical Engineering
September 2010 - September 2013
University of Tehran
Field of study
  • Remote Sensing
September 2006 - September 2010
University of Tehran
Field of study
  • Surveying and Geomatics Engineering

Publications

Publications (104)
Article
Full-text available
Abstract Wetlands are important ecosystems around the world, although they are degraded due both to anthropogenic and natural process. Newfoundland is among the richest Canadian province in terms of different wetland classes. Herbaceous wetlands cover extensive areas of the Avalon Peninsula, which are the habitat of a number of animal and plant spe...
Article
Wetlands provide a wide variety of environmental services globally and detailed wetland inventory maps are always necessary to determine the conservation strategies and effectively monitor these productive ecosystems. During the last two decades, satellite remote sensing data have been extensively used for wetland mapping and monitoring worldwide....
Article
Full-text available
Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution a...
Article
Full-text available
Despite recent research into the Interferometric Synthetic Aperture Radar (InSAR) technique for wetland mapping worldwide, its capability has not yet been thoroughly investigated for Canadian wetland ecosystems. Accordingly, this study statistically analysed interferometric coherence and SAR backscatter variation in a study area located on the Aval...
Article
Full-text available
Synthetic aperture radar (SAR) compact polarimetry (CP) systems are of great interest for large area monitoring because of their ability to acquire data in a wider swath compared to full polarimetry (FP) systems and a significant improvement in information content compared to single or dual polarimetry (DP) sensors. In this study, we compared the p...
Article
In Arctic offshore regions, the oil and gas hydrocarbons are transferred to the onshore basins through the subsea pipelines. However, the operational integrity of the subsea pipeline may be at risk of collision with traveling icebergs, which gouge the seabed in the Arctic shallow waters. Even though these sea bottom-founded structures are buried at...
Article
Full-text available
Sea ice profoundly influences ocean circulation, the polar environment, biology, climate, and commercial activities. The rapidly changing sea ice environment and increased human activities in polar regions drive the demand for sea ice monitoring. Spaceborne synthetic aperture radar (SAR) has been widely adopted for sea ice sensing due to its all-we...
Article
Full-text available
Providing an accurate above-ground biomass (AGB) map is of paramount importance for carbon stock and climate change monitoring. The main objective of this study is to compare the performance of pixel-based and object-based approaches for AGB estimation of temperate forests in north-eastern of New York State. Second, the capabilities of optical, SAR...
Article
Full-text available
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
Full-text available
Wetland is one of the most productive resources on earth, and it provides vital habitats for several unique species of flora and fauna. Over the last decade, mapping and monitoring wetlands by utilizing deep learning (DL) models and remote sensing data gained popularity due to the importance of wetland preservation. In general, DL-based methods hav...
Article
Full-text available
Seasonal variations result in hydrophytes and undrained hydric soil changes in wetland areas, which lead to a dynamic environment that makes wetland classification challenging. This study aims to explore the applicability of multi-seasonal Gray-Level Co-Occurrence Matrix (GLCM) texture-derived features for object-based wetland classification over l...
Article
Full-text available
In wetland mapping, a lot of uncertainty is related to the task of selecting an appropriate classification approach. Although the individual models are available and well-established in the literature for the classification task, the combination approaches have become popular recently. Hence, selecting an appropriate method is challenging, whether...
Article
Full-text available
The above-ground biomass (AGB) estimation monitoring provides a powerful tool for the assessment of carbon emission and sequestration. Using remote sensing technique is an environmentally friendly way of biomass estimation. Thus, this paper investigated optical (i.e. Landsat 8 OLI and Sentinel-2), synthetic aperture radar (SAR) (global phased array...
Article
Full-text available
Carbon sequestration coupled with flood mitigation and other functions of wetlands, such as water filtration, coastal protection, biodiversity, and providing recreational spots, make wetland mapping and monitoring important for different countries. Google Earth Engine (GEE) cloud computing platform is becoming a very important tool for lots of envi...
Conference Paper
Ice gouging is one of the major menaces to the subsea pipelines crossing the Arctic (e.g., Beaufort Sea) or the non-Arctic (e.g., Caspian Sea) shallow waters. Burial of the sea-bottom-founded infrastructures is regarded as a feasible method for protection of the subsea assets against the ice gouging threat. These pipelines are commonly embedded und...
Article
Full-text available
Sea ice monitoring plays a vital role in secure navigation and offshore activities. Synthetic aperture radar (SAR) has been widely used as an effective tool for sea ice remote sensing (e.g., ice type classification, concentration and thickness retrieval) for decades because it can collect data by day and night and in almost all weather conditions....
Article
Full-text available
Due to anthropogenic and natural activities, the land surface continuously changes over time. The accurate and timely detection of changes is greatly important for environmental monitoring , resource management and planning activities. In this study, a novel deep learning-based change detection algorithm is proposed for bi-temporal polarimetric syn...
Article
Full-text available
The use of machine learning algorithms to classify complex landscapes has been revolutionized by the introduction of deep learning techniques, particularly in remote sensing. Convolutional neural networks (CNNs) have shown great success in the classification of complex high-dimensional remote sensing imagery, specifically in wetland classification....
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...
Article
Full-text available
The emergence of deep learning techniques has revolutionized the use of machine learning algorithms to classify complicated environments, notably in remote sensing. Convolutional Neural Networks (CNNs) have shown considerable promise in classifying challenging high-dimensional remote sensing data, particularly in the classification of wetlands. Sta...
Article
Full-text available
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...
Article
Full-text available
Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the ot...
Article
Full-text available
Due to anthropogenic activities and climate change, many natural ecosystems, especially wetlands, are lost or changing at a rapid pace. For the last decade, there has been increasing attention towards developing new tools and methods for the mapping and classification of wetlands using remote sensing. At the same time, advances in artificial intell...
Article
Full-text available
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...
Article
Full-text available
Given the key role wetlands play in climate regulation and shoreline stabilization, identifying their spatial distribution is essential for the management, restoration, and protection of these invaluable ecosystems. The increasing availability of high spatial and temporal resolution optical and synthetic aperture radar (SAR) remote sensing data cou...
Article
Full-text available
Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-bas...
Article
Full-text available
While deep learning models have been extensively applied to land-use land-cover (LULC) problems, it is still a relatively new and emerging topic for separating and classifying wetland types. On the other hand, ensemble learning has demonstrated promising results in improving and boosting classification accuracy. Accordingly, this study aims to deve...
Article
Full-text available
Wetlands are among the most important, yet in danger ecosystems and play a vital role for the well-being of humans as well as flora and fauna. Over the past few years, state-of-the-art deep learning (DL) tools have gained attention for wetland classification within the remote sensing community. However, the DL methods could have complex structure a...
Article
Full-text available
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...
Article
Full-text available
Marine debris is considered a threat to the inhabitants, as well as the marine environments. Accumulation of marine debris, besides climate change factors, including warming water, sea-level rise, and changes in oceans’ chemistry, are causing the potential collapse of the marine environment’s health. Due to the increase of marine debris, including...
Article
Full-text available
Rising sea level is generally assumed and widely reported to be the significant driver of coastal erosion of most low-lying sandy beaches globally. However, there is limited data-driven evidence of this relationship due to the challenges in quantifying shoreline dynamics at the same temporal scale as sea-level records. Using a Google Earth Engine (...
Article
Full-text available
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....
Article
Full-text available
The Conne River watershed is dominated by wetlands that provide valuable ecosystem services, including contributing to the survivability and propagation of Atlantic salmon, an important subsistence species that has shown a dramatic decline over the past 30 years. To better understand and improve the management of the watershed, and in turn, the Atl...
Article
Full-text available
With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding o...
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
Full-text available
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...
Article
Full-text available
Soil salinity, a significant environmental indicator, is considered one of the leading causes of land degradation, especially in arid and semi-arid regions. In many cases, this major threat leads to loss of arable land, reduces crop productivity, groundwater resources loss, increases economic costs for soil management, and ultimately increases the...
Article
Full-text available
Surface water quality is degrading continuously both due to natural and anthropogenic causes. There are several indicators of water quality, among which sediment loading is mainly determined by turbidity. Normalized Difference Water Index (NDWI) is one indirect measure of sediments present in water. This study focuses on detecting and monitoring se...
Article
Full-text available
Wetlands are highly productive ecosystems that offer unique services on regional and global scales including nutrient assimilation, carbon reduction, geochemical cycling, and water storage. In recent years, however, they are being lost or exploited as croplands due to natural or man-made stressors (1.4 percent in 5 years within the USA). This decli...
Article
Full-text available
Forest is one of the most crucial Earth’s resources. Forest above-ground biomass (AGB) mapping has been research endeavors for a long time in many applications since it provides valuable information for carbon cycle monitoring, deforestation, and forest degradation monitoring. A methodology to rapidly and accurately estimate AGB is essential for fo...
Article
Shrub willow is considered an important dedicated energy crop in temperate climates for the production of bioenergy, biofuels, and bio-based products. A methodology to rapidly and accurately estimate above-ground biomass (AGB) is essential for understanding potential biomass supply, identifying potential growth limitations, and making management de...
Article
Full-text available
Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify co...
Article
In recent years, access to freely available and commercial satellite imagery, such as Sentinel-1, RADARSAT-2, COSMO-SkyMed, and TerrsSAR-X, increased to the level where most global waters are observed at least once per day by one of these satellite platforms. The availability of this data combined with technological advancements in machine-learning...
Article
Full-text available
Canada’s Earth-observing RADARSAT Constellation Mission (RCM) is intended to serve operational users. The users’ main objectives were to have routinely available high-quality quantitative information about their applications, with large area coverage potential. That two-part requirement was sufficient to establish an innovative synthetic aperture r...
Article
Full-text available
Due to the advent of powerful parallel processing tools, including modern Graphics Processing Units (GPU), new deep learning algorithms, such as Convolutional Neural Networks (CNNs), have significantly altered the state-of-the-art algorithms in satellite classification of complex environments. Recent studies have demonstrated that the generic featu...
Article
Full-text available
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...
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...
Article
Thanks to increasing urban development, it has become important for municipalities to understand how ecological processes function. In particular, urban wetlands are vital habitats for the people and the animals living amongst them. This is because wetlands provide great services, including water filtration, flood and drought mitigation, and recrea...
Article
Full-text available
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...
Article
Full-text available
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...
Article
Full-text available
Canada's successful space-based earth-observation (EO) radar program has earned widespread and expanding user acceptance following the launch of RADARSAT-1 in 1995. RADARSAT-2, launched in 2007, while providing data continuity for its predecessor's imaging capabilities, added new polarimetric modes. Canada's follow-up program, the RADARSAT Constell...