IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
As discussed in the previous part of this review paper, Remote Sensing (RS) creates unprecedented... more As discussed in the previous part of this review paper, Remote Sensing (RS) creates unprecedented opportunities by providing a variety of systems with different characteristics to study and monitor oceans. Part 1 of this review paper was dedicated to reviewing passive RS systems and their main applications in the ocean. Here, in part 2, seven active RS systems, including scatterometers, altimeters, gravimeters, Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR), Sound Navigation and Ranging (SONAR), High-Frequency (HF) radars are comprehensively reviewed. For consistency, this part is structured similarly to part 1. The aforementioned systems, along with their characteristics and primary applications, are introduced in separate sections. This review paper provides useful information to all students and researchers who are interested in the oceanographic applications of active RS systems.
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Reliable, accurate, and timely information about oceans is important for many applications, inclu... more Reliable, accurate, and timely information about oceans is important for many applications, including water resource management, hydrological cycle monitoring, environmental studies, agricultural and ecosystem health applications, economy, and the overall health of the environment. In this regard, remote sensing (RS) systems offer exceptional advantages for mapping and monitoring various oceanographic parameters with acceptable temporal and spatial resolutions over the oceans and coastal areas. So far, different methods have been developed to study oceans using various RS systems. This urges the necessity of having review studies that comprehensively discuss various RS systems, including passive and active sensors, and their advantages and limitations for ocean applications. In this article, the goal is to review most RS systems and approaches that have been worked on marine applications. This review paper is divided into two parts. Part 1 is dedicated to the passive RS systems for ocean studies. As such, four primary passive systems, including optical, thermal infrared radiometers, microwave radiometers, and Global Navigation Satellite Systems, are comprehensively discussed. Additionally, this article summarizes the main passive RS sensors and satellites, which have been utilized for different oceanographic applications. Finally, various oceanographic parameters, which can be retrieved from the data acquired by passive RS systems, along with the corresponding methods, are discussed.
Iran is among the driest countries in the world, where many natural hazards, such as floods, freq... more Iran is among the driest countries in the world, where many natural hazards, such as floods, frequently occur. This study introduces a straightforward flood hazard assessment approach using remote sensing datasets and Geographic Information Systems (GIS) environment in an area located in the western part of Iran. Multiple GIS and remote sensing datasets, including Digital Elevation Model (DEM), slope, rainfall, distance from the main rivers, Topographic Wetness Index (TWI), Land Use/Land Cover (LULC) maps, soil type map, Normalized Difference Vegetation Index (NDVI), and erosion rate were initially produced. Then, all datasets were converted into fuzzy values using a linear fuzzy membership function. Subsequently, the Analytical Hierarchy Process (AHP) technique was applied to determine the weight of each dataset, and the relevant weight values were then multiplied to fuzzy values. Finally, all the processed parameters were integrated using a fuzzy analysis to produce the flood haza...
A novel unsupervised forest change detection method based on the integration of a multiresolution singular value decomposition fusion and an edge-aware Markov Random Field algorithm
Trends of CO and NO2 Pollutants Change in Iran during Covid-19 Pandemic using Time-Series Sentinel-5 Images in Google Earth Engine
The first cases of Covid-19 in Iran were reported shortly after the disease outbreak in Wuhan, Ch... more The first cases of Covid-19 in Iran were reported shortly after the disease outbreak in Wuhan, China. The end of the Persian year and the beginning of the Nowruz holidays in the following year (March 2020) coincided with its global pandemic, which led to quarantine and lockdown in the country. Many studies have shown that with the spread of this disease and the decline of industrial activities, environmental pollutants were drastically reduced. Among these pollutants, Nitrogen Dioxide (NO2) and Carbon Monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes of these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three time periods from March 11 to April 8 of 2019, 2020, and 2021 were investigated. To this end, time-series of the Sentinel-5P TROPOMI and in-situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results obtained from the satel...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
This article compares the performances of the most commonly used keypoint detectors and descripto... more This article compares the performances of the most commonly used keypoint detectors and descriptors (SIFT, SURF, KAZE, AKAZE, ORB, and BRISK) in keypoint-based relative radiometric normalization (RRN) of unregistered bitemporal multispectral images. The keypoints matched between subject and reference images represent possible unchanged regions and form a radiometric control set (RCS). The initial RCS is further refined by removing the matched keypoints with a low cross-correlation. The final RCS is used to approximate a linear mapping between the corresponding bands of the subject and reference images. This procedure is validated on five datasets of unregistered multispectral image pairs acquired by inter/intra sensors in terms of RRN accuracy, visual quality, quality, and quantity of the samples in the RCS, and computational time. The experimental results show that keypoint-based RRN is robust against variations in spatial-resolution, illumination, and sensors. The blob detectors (SURF, SIFT, KAZE, and AKAZE) are more accurate on average than the corner detectors (ORB and BRISK) in RRN, with an expense of higher computational cost. The source code and samples of datasets used in this study are made available at https://github.com/ArminMoghimi/ keypoint-based-RRN to support reproducible research in remote sensing.
A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importa... more A large portion of Canada is covered by wetlands; mapping and monitoring them is of great importance for various applications. In this regard, Remote Sensing (RS) technology has been widely employed for wetland studies in Canada over the past 45 years. This study evaluates meta-data to investigate the status and trends of wetland studies in Canada using RS technology by reviewing the scientific papers published between 1976 and the end of 2020 (300 papers in total). Initially, a meta-analysis was conducted to analyze the status of RS-based wetland studies in terms of the wetland classification systems, methods, classes, RS data usage, publication details (e.g., authors, keywords, citations, and publications time), geographic information, and level of classification accuracies. The deep systematic review of 128 peer-reviewed articles illustrated the rising trend in using multi-source RS datasets along with advanced machine learning algorithms for wetland mapping in Canada. It was als...
Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI); 21-Year Drought Monitoring in Iran using Satellite Imagery within Google Earth Engine
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
The first Canadian Wetland Inventory (CWI) map, which was based on Landsat data, was produced in ... more The first Canadian Wetland Inventory (CWI) map, which was based on Landsat data, was produced in 2019 using the Google Earth Engine (GEE) big data processing platform. The proposed GEE-based method to create the preliminary CWI map proved to be a cost-, time-, and computationally efficient approach. Although the initial effort to produce an CWI map was valuable with a 71% Overall Accuracy (OA), there were several inevitable limitations (e.g., low-quality samples for training and validation of the map). Therefore, it was important to comprehensively investigate those limitations and develop effective solutions to improve the accuracy of the Landsat-based CWI (L-CWI) map. Over the past year, the L-CWI map was shared with several governmental, academic, environmental nonprofit, and industrial organizations. Subsequently, valuable feedback was received on the accuracy of this product by comparing it with various in-situ data, photo-interpreted reference samples, Land Cover/Land Use (LCLU) maps, and highresolution aerial images. It was generally observed that the accuracy of the L-CWI map was lower relative to the other available products. For example, the average OA in four Canadian provinces using in-situ data was 60%. Moreover, including reliable in-situ data, using an object-based classification method, and adding more optical and Synthetic Aperture RADAR (SAR) datasets were identified as the main practical solutions to improve the CWI map in the future. Finally, limitations and solutions discussed in this study are applicable to any large-scale wetland mapping using remote sensing methods, especially to CWI generation using optical satellite data in GEE.
The ability of the Canadian agriculture sector to make better decisions and manage its operations... more The ability of the Canadian agriculture sector to make better decisions and manage its operations more competitively in the long term is only as good as the information available to inform decision-making. At all levels of Government, a reliable flow of information between scientists, practitioners, policy-makers, and commodity groups is critical for developing and supporting agricultural policies and programs. Given the vastness and complexity of Canada’s agricultural regions, space-based remote sensing is one of the most reliable approaches to get detailed information describing the evolving state of the country’s environment. Agriculture and Agri-Food Canada (AAFC)—the Canadian federal department responsible for agriculture—produces the Annual Space-Based Crop Inventory (ACI) maps for Canada. These maps are valuable operational space-based remote sensing products which cover the agricultural land use and non-agricultural land cover found within Canada’s agricultural extent. Devel...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managin... more Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of Manuscript
A Novel Radiometric Control Set Sample Selection Strategy for Relative Radiometric Normalization of Multitemporal Satellite Images
IEEE Transactions on Geoscience and Remote Sensing
This article presents a new relative radiometric normalization (RRN) method for multitemporal sat... more This article presents a new relative radiometric normalization (RRN) method for multitemporal satellite images based on the automatic selection and multistep optimization of the radiometric control set samples (RCSS). A novel image-fusion strategy based on the fast local Laplacian filter is employed to generate a difference index using the complementary information extracted from the change vector analysis and absolute gradient difference of the bitemporal satellite images. The difference index is then segmented into changed and unchanged pixels using a fast level-set method. A novel local outlier method is then applied to the unchanged pixels of the bitemporal images to identify the initial RCSS, which are then scored by a novel unchanged purity index, and the histogram of the scores is used to produce the final RCSS. The RRN between the bitemporal images is achieved by adjusting the subject image to the reference image using orthogonal linear regression on the final RCSS. The proposed method is applied to seven different data sets comprised of bitemporal images acquired by various satellites, including Landsat TM/ETM+, Sentinel 2B, Worldview 2/3, and Aster. The experimental results show that the method outperforms the state-of-the-art RRN methods. It reduces the average root-mean-square error (RMSE) of the best baseline method (IR-MAD) by up to 32% considering all data sets.
Monitoring Land use changes is one of the important applications of remote sensing and geographic... more Monitoring Land use changes is one of the important applications of remote sensing and geographic information system. In this study, a framework for change monitoring in multitemporal satellite images is presented by Iteratively Reweighted multivariate alteration detection (IR-MAD) algorithm and support vector machine (SVM) classification. In this study, the change detection analysis has been done using multitemporal Landsat satellite images with 18 years time interval of Shahi Island and a part of the western region of Lake Urmia. The proposed method has two main steps in change monitoring. In the first step, components of change intensities are determined automatically by IR-MAD transformation. In the following, optimized components are selected by applying the kernel principal component analysis (KPCA) on components of change intensities. In the next step, for generating the content of change map, The combination of optimal components is classified by SVM method. For the evaluation performance of the proposed method, in change monitoring, this method was compared with conventional methods such as analysis of the spectral-temporal combination and post classification comparison. The experimental results show that the overall accuracy of the proposed method increased 4.89% and 4.39% compared to that of the spectral-temporal Combination and post classification comparison, respectively.
In this study, a method for unsupervised change detection in multi-temporal SAR images has been p... more In this study, a method for unsupervised change detection in multi-temporal SAR images has been presented based on integrating clustering and active contour model. In this method, texture information is extracted by using Gabor filter in different scales and directions. KPCA transformation is also applied to reduce the dependency between the extracted features and image information. Moreover, Discrete Wavelet Transformation (DWT) and Gustafson-Kessel clustering (GKC) methods are used respectively to generate the difference image and the initial contour for the active contour model. In the final step, the region-based nonparametric active contour model is used for producing the change image containing changed and unchanged regions. For performance evaluation of the proposed method, two sets of high resolution multi-temporal TerraSAR-X images are considered. Experimental results of unsupervised change detection method show that, the total error rate of the proposed approach for the first data set are decreased respectively to 4.95%, 3.30% and 3.34% compared to that of the Chan-Vese, MRF and EMMRF methods and for the second data set, the total error rate of the proposed method are decreased to 2.56%, 1.86% and 1.87 As well. Moreover, the results showed that the use of GKC method leads to production of the initial curve with minimal convergence time for the active contour model. Also, the use of active contour model improves the accuracy of change map creation using a repititive process.
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Papers by armin moghimi