ABSTRACT Heyelan duyarlılık haritalarının üretilmesi ve heyelan riski taşıyan alanların belirlenm... more ABSTRACT Heyelan duyarlılık haritalarının üretilmesi ve heyelan riski taşıyan alanların belirlenmesi afet planlamaları için kritik bir işlem adımıdır. Üretilen haritaların doğruluğu can ve mal kayıplarının azaltılması açısından büyük önem arz etmektedir. Heyelan riski taşıyan alanların belirlenmesi yeryüzünün farklı özelliklerini gösteren katmanların bir arada değerlendirilmesini gerektirmektedir. Bu amaçla, günümüze kadar duyarlılık haritalarının doğruluğunun artırılmasına yönelik birçok algoritma geliştirilerek uygulamalarda kullanılmıştır. Heyelan duyarlılık haritalarının oluşturulmasında son yıllarda kullanılan regresyon ağaçları, akış şemalarına benzeyen yapılarıyla birçok regresyon probleminde başarıyla kullanılmıştır. Bu çalışmada, Trabzon iline ait litoloji, eğim, arazi örtüsü, bakı, yükseklik ve yol ağı katmanlarından faydalanılarak regresyon ağaçları yönteminin heyelan risk potansiyelinin analizindeki etkinliği araştırılmıştır. Yöntemin performansı literatürde yaygın olarak kullanılan lojistik regresyon modeli ile karşılaştırılmıştır. Bu çalışmada üretilen sonuçlar, regresyon ağaçları ile üretilen duyarlılık haritasının doğruluğunun lojistik regresyon yöntemiyle üretilene göre %5 daha yüksek olduğunu göstermiştir. Sonuç olarak, regresyon ağaçları yönteminin çok değişkenli konumsal verilerin modellenmesinde etkin bir yaklaşım olduğu belirlenmiştir.
Advances in natural and technological hazards research, Jun 29, 2018
Machine learning techniques have been increasingly employed for solving many scientific and engin... more Machine learning techniques have been increasingly employed for solving many scientific and engineering problems. These data driven methods have been lately utilized with great success to produce landslide susceptibility maps. They give promising results particularly for mapping large landslide prone areas with limited geotechnical data. This chapter surveys their use in landslide susceptibility analysis and presents a case study investigating their effectiveness with regard to a conventional statistical method, namely logistic regression. It starts with the importance of spatial prediction of future landslides from past and present ones and discusses the requirement of advanced techniques for landslide susceptibility mapping. A critical literature survey is given under five main categories including core algorithms and their ensembles together with their hybrid forms. An application is presented for machine learning application using bagging, random forest, rotation forest and support vector machines with their optimal settings.
International Journal of Remote Sensing, Mar 5, 2013
ABSTRACT Increasing the accuracy of thematic maps produced through the process of image classific... more ABSTRACT Increasing the accuracy of thematic maps produced through the process of image classification has been a hot topic in remote sensing. For this aim, various strategies, classifiers, improvements, and their combinations have been suggested in the literature. Ensembles that combine the prediction of individual classifiers with weights based on the estimated prediction accuracies are strategies aiming to improve the classifier performances. One of the recently introduced ensembles is the rotation forest, which is based on the idea of building accurate and diverse classifiers by applying feature extraction to the training sets and then reconstructing new training sets for each classifier. In this study, the effectiveness of the rotation forest was investigated for decision trees in land-use and land-cover LULC mapping, and its performance was compared with performances of the six most widely used ensemble methods. The results were verified for the effectiveness of the rotation forest ensemble as it produced the highest classification accuracies for the selected satellite data. When the statistical significance of differences in performances was analysed using McNemar's tests based on normal and chi-squared distributions, it was found that the rotation forest method outperformed the bagging, Diverse Ensemble Creation by Oppositional Relabelling of Artificial Training Examples DECORATE, and random subspace methods, whereas the performance differences with the other ensembles were statistically insignificant.
ABSTRACT Identification of landslides and production of landslide susceptibility maps are crucial... more ABSTRACT Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area.
Journal of The Indian Society of Remote Sensing, Jul 19, 2018
Object-based image analysis (OBIA) has attained great importance for the delineation of landscape... more Object-based image analysis (OBIA) has attained great importance for the delineation of landscape features, particularly with the accessibility to satellite images with high spatial resolution acquired by recent sensors. Statistical parametric classifiers have become ineffective mainly due to their assumption of normal distribution, vast increase in the dimensions of the data and availability of limited ground sample data. Despite pixel-based approaches, OBIA takes semantic information of extracted image objects into consideration, and thus provides more comprehensive image analysis. In this study, Indian Pines hyperspectral data set, which was recorded by the AVIRIS hyperspectral sensor, was used to analyse the effects of high dimensional data with limited ground reference data. To avoid the dimensionality curse, principal component analysis (PCA) and feature selection based on Jeffries–Matusita (JM) distance were utilized. First 19 principal components representing 98.5% of the image were selected using the PCA technique whilst 30 spectral bands of the image were determined using JM distance. Nearest neighbour (NN) and random forest (RF) classifiers were employed to test the performances of pixel- and object-based classification using conventional accuracy metrics. It was found that object-based approach outperformed the traditional pixel-based approach for all cases (up to 18% improvement). Also, the RF classifier produced significantly more accurate results (up to 10%) than the NN classifier.
Machine learning algorithms reported to be robust and superior to the conventional parametric cla... more Machine learning algorithms reported to be robust and superior to the conventional parametric classifiers have been recently employed in object-based classification. Within these algorithms, ensemble learning methods that construct set of individual classifiers and combining their predictions to make final decision about unlabelled data have been successfully applied. In this study, performance and effectiveness of a novel ensemble learning algorithm, rotation forest (RotFor) aiming to build diverse and accurate classifiers, was investigated for the first time in object-based classification using a WorldView-2 (WV-2) satellite image. Also, the combination of satellite imagery and ancillary data (i.e. normalized difference vegetation index and principal components) were assessed. Random forest (RF), support vector machine (SVM) and nearest neighbour (NN) algorithms were also used as benchmark classifiers to evaluate the power of RotFor. The classification results confirmed that integration of ancillary data increased the classification accuracy in comparison to using solely spectral bands of WV-2. While RotFor and SVM generally produced similar results, they outperformed the RF and NN based on McNemar’s and Wilcoxon’s signed-rank test of statistical significance results.
Abstract Image classification is a complex process, the accuracy of which is mainly related to th... more Abstract Image classification is a complex process, the accuracy of which is mainly related to the characteristics of the dataset, complexity of the problem under analysis, and the robustness of the classification algorithm. This study aims to assess and validate a recent classifier ensemble method called canonical correlation forest (CCF) for object-based image analysis. The proposed methodology is based on building a decision forest ensemble model using a set of decision trees constructed with the dataset obtained as a result of canonical correlation analysis. Its performance was compared to conventional nearest neighbor (NN) and popular random forest (RF) algorithms in the classification of WorldView-2 imagery. The number of image object features (total 128 features) was reduced to 42 features using a correlation-based feature selection algorithm. The results of the study showed that the CCF algorithm was found to be superior to RF and NN algorithms with respect to the estimated accuracy measures. The improvement in accuracy reached 4% for both 42- and 128-feature datasets and this level of improvement was found to be statistically significant when considering against the McNemar’s test results. Moreover, the performances of the CCF and NN algorithms were found to be sensitive for the dimension of the input dataset, whilst the performance of RF was more stable with or without feature selection. To sum up, the CCF algorithm, introduced as a new member of the tree-based ensemble learning algorithms, was found to be a powerful alternative to the RF algorithm considered dataset used in this study.
International Journal of Engineering and Geosciences
Lately, unmanned aerial vehicle (UAV) become a prominent technology in remote sensing studies wit... more Lately, unmanned aerial vehicle (UAV) become a prominent technology in remote sensing studies with the advantage of high-resolution, low-cost, rapidly and periodically achievable three-dimensional (3D) data. UAV enables data capturing in different flight altitudes, imaging geometries, and viewing angles which make detailed monitoring and modelling of target objects possible. Against earlier times, UAVs have been improved by integrating real-time kinematic (RTK) positioning and multispectral (MS) imaging equipment. In this study, positioning accuracy and land cover classification potential of RTK equipped MS UAVs were evaluated by point-based geolocation accuracy analysis and pixel-based ensemble learning algorithms. In positioning accuracy evaluation, ground control points (GCPs), pre-defined by terrestrial global navigation satellite system (GNSS) measurements, were used as the reference data while Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms were applied f...
ABSTRACT Heyelan duyarlılık haritalarının üretilmesi ve heyelan riski taşıyan alanların belirlenm... more ABSTRACT Heyelan duyarlılık haritalarının üretilmesi ve heyelan riski taşıyan alanların belirlenmesi afet planlamaları için kritik bir işlem adımıdır. Üretilen haritaların doğruluğu can ve mal kayıplarının azaltılması açısından büyük önem arz etmektedir. Heyelan riski taşıyan alanların belirlenmesi yeryüzünün farklı özelliklerini gösteren katmanların bir arada değerlendirilmesini gerektirmektedir. Bu amaçla, günümüze kadar duyarlılık haritalarının doğruluğunun artırılmasına yönelik birçok algoritma geliştirilerek uygulamalarda kullanılmıştır. Heyelan duyarlılık haritalarının oluşturulmasında son yıllarda kullanılan regresyon ağaçları, akış şemalarına benzeyen yapılarıyla birçok regresyon probleminde başarıyla kullanılmıştır. Bu çalışmada, Trabzon iline ait litoloji, eğim, arazi örtüsü, bakı, yükseklik ve yol ağı katmanlarından faydalanılarak regresyon ağaçları yönteminin heyelan risk potansiyelinin analizindeki etkinliği araştırılmıştır. Yöntemin performansı literatürde yaygın olarak kullanılan lojistik regresyon modeli ile karşılaştırılmıştır. Bu çalışmada üretilen sonuçlar, regresyon ağaçları ile üretilen duyarlılık haritasının doğruluğunun lojistik regresyon yöntemiyle üretilene göre %5 daha yüksek olduğunu göstermiştir. Sonuç olarak, regresyon ağaçları yönteminin çok değişkenli konumsal verilerin modellenmesinde etkin bir yaklaşım olduğu belirlenmiştir.
Advances in natural and technological hazards research, Jun 29, 2018
Machine learning techniques have been increasingly employed for solving many scientific and engin... more Machine learning techniques have been increasingly employed for solving many scientific and engineering problems. These data driven methods have been lately utilized with great success to produce landslide susceptibility maps. They give promising results particularly for mapping large landslide prone areas with limited geotechnical data. This chapter surveys their use in landslide susceptibility analysis and presents a case study investigating their effectiveness with regard to a conventional statistical method, namely logistic regression. It starts with the importance of spatial prediction of future landslides from past and present ones and discusses the requirement of advanced techniques for landslide susceptibility mapping. A critical literature survey is given under five main categories including core algorithms and their ensembles together with their hybrid forms. An application is presented for machine learning application using bagging, random forest, rotation forest and support vector machines with their optimal settings.
International Journal of Remote Sensing, Mar 5, 2013
ABSTRACT Increasing the accuracy of thematic maps produced through the process of image classific... more ABSTRACT Increasing the accuracy of thematic maps produced through the process of image classification has been a hot topic in remote sensing. For this aim, various strategies, classifiers, improvements, and their combinations have been suggested in the literature. Ensembles that combine the prediction of individual classifiers with weights based on the estimated prediction accuracies are strategies aiming to improve the classifier performances. One of the recently introduced ensembles is the rotation forest, which is based on the idea of building accurate and diverse classifiers by applying feature extraction to the training sets and then reconstructing new training sets for each classifier. In this study, the effectiveness of the rotation forest was investigated for decision trees in land-use and land-cover LULC mapping, and its performance was compared with performances of the six most widely used ensemble methods. The results were verified for the effectiveness of the rotation forest ensemble as it produced the highest classification accuracies for the selected satellite data. When the statistical significance of differences in performances was analysed using McNemar's tests based on normal and chi-squared distributions, it was found that the rotation forest method outperformed the bagging, Diverse Ensemble Creation by Oppositional Relabelling of Artificial Training Examples DECORATE, and random subspace methods, whereas the performance differences with the other ensembles were statistically insignificant.
ABSTRACT Identification of landslides and production of landslide susceptibility maps are crucial... more ABSTRACT Identification of landslides and production of landslide susceptibility maps are crucial steps that can help planners, local administrations, and decision makers in disaster planning. Accuracy of the landslide susceptibility maps is important for reducing the losses of life and property. Models used for landslide susceptibility mapping require a combination of various factors describing features of the terrain and meteorological conditions. Many algorithms have been developed and applied in the literature to increase the accuracy of landslide susceptibility maps. In recent years, geographic information system-based multi-criteria decision analyses (MCDA) and support vector regression (SVR) have been successfully applied in the production of landslide susceptibility maps. In this study, the MCDA and SVR methods were employed to assess the shallow landslide susceptibility of Trabzon province (NE Turkey) using lithology, slope, land cover, aspect, topographic wetness index, drainage density, slope length, elevation, and distance to road as input data. Performances of the methods were compared with that of widely used logistic regression model using ROC and success rate curves. Results showed that the MCDA and SVR outperformed the conventional logistic regression method in the mapping of shallow landslides. Therefore, multi-criteria decision method and support vector regression were employed to determine potential landslide zones in the study area.
Journal of The Indian Society of Remote Sensing, Jul 19, 2018
Object-based image analysis (OBIA) has attained great importance for the delineation of landscape... more Object-based image analysis (OBIA) has attained great importance for the delineation of landscape features, particularly with the accessibility to satellite images with high spatial resolution acquired by recent sensors. Statistical parametric classifiers have become ineffective mainly due to their assumption of normal distribution, vast increase in the dimensions of the data and availability of limited ground sample data. Despite pixel-based approaches, OBIA takes semantic information of extracted image objects into consideration, and thus provides more comprehensive image analysis. In this study, Indian Pines hyperspectral data set, which was recorded by the AVIRIS hyperspectral sensor, was used to analyse the effects of high dimensional data with limited ground reference data. To avoid the dimensionality curse, principal component analysis (PCA) and feature selection based on Jeffries–Matusita (JM) distance were utilized. First 19 principal components representing 98.5% of the image were selected using the PCA technique whilst 30 spectral bands of the image were determined using JM distance. Nearest neighbour (NN) and random forest (RF) classifiers were employed to test the performances of pixel- and object-based classification using conventional accuracy metrics. It was found that object-based approach outperformed the traditional pixel-based approach for all cases (up to 18% improvement). Also, the RF classifier produced significantly more accurate results (up to 10%) than the NN classifier.
Machine learning algorithms reported to be robust and superior to the conventional parametric cla... more Machine learning algorithms reported to be robust and superior to the conventional parametric classifiers have been recently employed in object-based classification. Within these algorithms, ensemble learning methods that construct set of individual classifiers and combining their predictions to make final decision about unlabelled data have been successfully applied. In this study, performance and effectiveness of a novel ensemble learning algorithm, rotation forest (RotFor) aiming to build diverse and accurate classifiers, was investigated for the first time in object-based classification using a WorldView-2 (WV-2) satellite image. Also, the combination of satellite imagery and ancillary data (i.e. normalized difference vegetation index and principal components) were assessed. Random forest (RF), support vector machine (SVM) and nearest neighbour (NN) algorithms were also used as benchmark classifiers to evaluate the power of RotFor. The classification results confirmed that integration of ancillary data increased the classification accuracy in comparison to using solely spectral bands of WV-2. While RotFor and SVM generally produced similar results, they outperformed the RF and NN based on McNemar’s and Wilcoxon’s signed-rank test of statistical significance results.
Abstract Image classification is a complex process, the accuracy of which is mainly related to th... more Abstract Image classification is a complex process, the accuracy of which is mainly related to the characteristics of the dataset, complexity of the problem under analysis, and the robustness of the classification algorithm. This study aims to assess and validate a recent classifier ensemble method called canonical correlation forest (CCF) for object-based image analysis. The proposed methodology is based on building a decision forest ensemble model using a set of decision trees constructed with the dataset obtained as a result of canonical correlation analysis. Its performance was compared to conventional nearest neighbor (NN) and popular random forest (RF) algorithms in the classification of WorldView-2 imagery. The number of image object features (total 128 features) was reduced to 42 features using a correlation-based feature selection algorithm. The results of the study showed that the CCF algorithm was found to be superior to RF and NN algorithms with respect to the estimated accuracy measures. The improvement in accuracy reached 4% for both 42- and 128-feature datasets and this level of improvement was found to be statistically significant when considering against the McNemar’s test results. Moreover, the performances of the CCF and NN algorithms were found to be sensitive for the dimension of the input dataset, whilst the performance of RF was more stable with or without feature selection. To sum up, the CCF algorithm, introduced as a new member of the tree-based ensemble learning algorithms, was found to be a powerful alternative to the RF algorithm considered dataset used in this study.
International Journal of Engineering and Geosciences
Lately, unmanned aerial vehicle (UAV) become a prominent technology in remote sensing studies wit... more Lately, unmanned aerial vehicle (UAV) become a prominent technology in remote sensing studies with the advantage of high-resolution, low-cost, rapidly and periodically achievable three-dimensional (3D) data. UAV enables data capturing in different flight altitudes, imaging geometries, and viewing angles which make detailed monitoring and modelling of target objects possible. Against earlier times, UAVs have been improved by integrating real-time kinematic (RTK) positioning and multispectral (MS) imaging equipment. In this study, positioning accuracy and land cover classification potential of RTK equipped MS UAVs were evaluated by point-based geolocation accuracy analysis and pixel-based ensemble learning algorithms. In positioning accuracy evaluation, ground control points (GCPs), pre-defined by terrestrial global navigation satellite system (GNSS) measurements, were used as the reference data while Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms were applied f...
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