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Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images

Taha Lamyaa Gamal El-deen, Ibrahim Rania Elsayed

Geomatics and Environmental Engineering

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2021

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Vol. 15, no. 4

101--116

EN

The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery. The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.

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An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq

Alwan Imzahim A., Aziz Nadia A.

Geomatics and Environmental Engineering

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2021

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Vol. 15, no. 1

5--21

EN

Land cover mapping of marshland areas from satellite images data is not a simple process, due to the similarity of the spectral characteristics of the land cover. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Sentinel 2B by ESA (European Space Agency) were used to classify the land cover of Al Hawizeh marsh/Iraq Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B images provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.

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Output Error Method for Tiltrotor Unstable in Hover

Lichota P., Szulczyk J.

Archive of Mechanical Engineering

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2017

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Vol. LXIV, nr 1

23--36

EN

This article investigates unstable tiltrotor in hover system identification from flight test data. The aircraft dynamics was described by a linear model defined in Body-Fixed-Coordinate System. Output Error Method was selected in order to obtain stability and control derivatives in lateral motion. For estimating model parameters both time and frequency domain formulations were applied. To improve the system identification performed in the time domain, a stabilization matrix was included for evaluating the states. In the end, estimates obtained from various Output Error Method formulations were compared in terms of parameters accuracy and time histories. Evaluations were performed in MATLAB R2009b environment.

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Helicity of Convective Flows from Localized Heat Source in a Rotating Layer

Sukhanovskii A., Evgrafova A., Popova E.

Archive of Mechanical Engineering

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2017

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Vol. LXIV, nr 2

177--188

EN

Experimental and numerical study of the steady-state cyclonic vortex from isolated heat source in a rotating fluid layer is described. The structure of laboratory cyclonic vortex is similar to the typical structure of tropical cyclones from observational data and numerical modelling including secondary flows in the boundary layer. Differential characteristics of the flow were studied by numerical simulation using CFD software Flow Vision. Helicity distribution in rotating fluid layer with localized heat source was analysed. Two mechanisms which play role in helicity generation are found. The first one is the strong correlation of cyclonic vortex and intensive upward motion in the central part of the vessel. The second one is due to large gradients of velocity on the periphery. The integral helicity in the considered case is substantial and its relative level is high.

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A comparison of three approaches to non-stationary flood frequency analysis

Debele S. E., Strupczewski W. G., Bogdanowicz E.

Acta Geophysica

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2017

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Vol. 65, no. 4

863--883

EN

Non-stationary flood frequency analysis (FFA) is applied to statistical analysis of seasonal flow maxima from Polish and Norwegian catchments. Three non-stationary estimation methods, namely, maximum likelihood (ML), two stage (WLS/TS) and GAMLSS (generalized additive model for location, scale and shape parameters), are compared in the context of capturing the effect of non-stationarity on the estimation of time-dependent moments and design quantiles. The use of a multimodel approach is recommended, to reduce the errors due to the model misspecification in the magnitude of quantiles. The results of calculations based on observed seasonal daily flow maxima and computer simulation experiments showed that GAMLSS gave the best results with respect to the relative bias and root mean square error in the estimates of trend in the standard deviation and the constant shape parameter, while WLS/TS provided better accuracy in the estimates of trend in the mean value. Within three compared methods the WLS/TS method is recommended to deal with non-stationarity in short time series. Some practical aspects of the GAMLSS package application are also presented. The detailed discussion of general issues related to consequences of climate change in the FFA is presented in the second part of the article entitled “Around and about an application of the GAMLSS package in non-stationary flood frequency analysis”.