Yazar "Jamali, A." seçeneğine göre listele

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    A COMPARISON of TREE-BASED ALGORITHMS for COMPLEX WETLAND CLASSIFICATION USING the GOOGLE EARTH ENGINE
    (International Society for Photogrammetry and Remote Sensing, 2021) Jamali, A.; Mahdianpari, M.; Karas, I.R.
    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 other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers. © Author(s) 2021. CC BY 4.0 License.
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    Land use land cover mapping using advanced machine learning classifiers
    (Sciendo, 2021) Jamali, A.
    Due to the recent climate changes such as floods and droughts, there is a need for Land Use Land Cover (LULC) mapping to monitor environmental changes that have effects on ecology, policy management, health and disaster management. As such, in this study, two well-known machine learning classifiers, namely, Support Vector Machine (SVM) and Random Forest (RF), are used for land cover mapping. In addition, two advanced deep learning algorithms, namely, the GAMLP and FSMLP, that are based on the Multi-layer Perceptron (MLP) function are developed in MATLAB programming language. The GAMLP uses a Genetic Algorithm (GA) to optimise parameters of the MLP function and, on the other hand, the FSMLP uses a derivative-free function for optimisation of the MLP function parameters. Three different scenarios using Landsat-8 imagery with spatial resolutions of 30 and 15 m are defined to investigate the effects of data pre-processing on the final predicted LULC map. Results based on the statistical indices, including overall accuracy (OA) and kappa index, show that the developed MLP-based algorithms have relatively high accuracies with higher than 98% correct classification. Besides the statistical indices, final LULC maps are interpreted visually where the GAMLP and FSMLP give the best results for the pre-processed Landsat-8 imagery with a spatial resolution of 15 m, but they have the worst outcomes for the unprocessed Landsat-8 imagery compared to SVM and RF classifiers visually and statistically. © 2021 Ali Jamali, published by Sciendo.
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    Topological 3D Spatial Interpolation Based on the Interval-Valued Homotopy Continuation
    (Springer Science and Business Media Deutschland GmbH, 2023) Jamali, A.; Castro, F.A.; Karas, I.R.
    Estimating unknown values using its surrounding measured values is called spatial interpolation, a vital tool for estimating continuous spatial data such as the earth’s surface. Construction of the Digital Elevation Model is one of the most common applications of spatial interpolation methods. There are various global and local interpolation techniques, including Kriging, Inverse Distance Weighted (IDW), Thiessen polygons (TIN), Natural Neighbor (NN), and Spline interpolation. This paper introduces the interval-valued homotopy continuation for 3D spatial data interpolation. Straight lines or algebraic curves can be reconstructed using homotopy continuation between any pairs of 3D data. The novel method of the interval-valued homotopy to restore the topographic surface between spatial data is developed in MATLAB programming language. For a dataset of ASTER GDEM, the presented mathematical algorithm shows better results compared to TIN and IDW methods in terms of Mean Squared Error, Mean Absolute Error, and Root Mean Squared Error with values of 5.2897, 1.53, and 2.299 m, respectively. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.