Improved building roof type classification using correlation-based feature selection and gain ratio algorithms

Of late, application of data mining for pattern recognition and feature classification is fast becoming an essential technique in remote sensing research. Accurate feature selection is a necessary step to improve the accuracy of classification. This process depends on the number of feature attributes available for interactive synthesis of common characteristics that discriminate different features. Geographic object-based image analysis (GEOBIA) has made it possible to derive varieties of object attribute for this purpose; however, the analysis is more computationally intensive. The aim of this study is to develop feature selection technique that will provide the most suitable attributes to identify different roofing materials and their conditions. First, the feature importance was evaluated using gain ratio algorithm, and the result was ranked, leading to selection of the optimal feature subset. Then, the quality of the selected features was assessed using correlation-based feature selection (CFS). The classification results using SVM classifier produced an overall accuracy of 83.16%. The study has shown that the ability to exploit rich image feature attribute through optimization process improves accurate extraction of roof material with greater reliability

Land transformation assessment using the integration of remote sensing and GIS techniques: a case study of Al-Anbar Province, Iraq

Human activities and climate changes significantly affect our environment, altering hydrologic cycles. Several environmental, social, political, and economical factors contribute to land transformation as well as environmental changes. This study first identified the most critical factors that affect the environment in Al-Anbar city including population growth, urbanization expansion, bare land expansion, and reduction in vegetation cover. The combination of remote sensing data and fuzzy analytic hierarch process (Fuzzy AHP) enabled exploration of land transformations and environmental changes in the study area during 2001 to 2013 in terms of long and short-term changes. Results of land transformation showed that the major changes in water bodies increased radically (94 %) from the long-term change in 2001 to 2013 because of water policies. In addition, the urban class expanded in two short-term periods (2001–2007 and 2007–2013), representing net changes of 46 and 60 %, respectively. Finally, barren land showed 25 % reduction in the first period because of the huge expansion of water in the lake; a small percentage of growth gain was observed in the second period. Based on the land transformation results, the environmental degradation assessment showed that the study area generally had high level of environmental degradation. The degradation was mostly in the center and the north part of the study area. This study suggested for further studies to include other factors that also responsible for environmental degradation such as water quality and desertification threatening

Surface runoff estimation and prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA and distributed-GIS-based SCS-CN models at tropical region

The integration of precipitation intensity and LULC forecasting have played a significant role in prospect surface runoff, allowing for an extension of the lead time that enables a more timely implementation of the control measures. The current study proposes a full-package model to monitor the changes in surface runoff in addition to forecasting the future surface runoff based on LULC and precipitation factors. On one hand, six different LULC classes from Spot-5 satellite image were extracted by object-based Support Vector Machine (SVM) classifier. Conjointly, Land Transformation Model (LTM) was used to detect the LULC pixel changes from 2000 to 2010 as well as predict the 2020. On the other hand, ARIMA model was applied to the analysis and forecasting the rainfall trends. The parameters of ARIMA time series model were calibrated and fitted statistically to minimize the prediction uncertainty by latest Taguchi method. Rainfall and streamflow data recorded in eight nearby gauging stations were engaged to train, forecast, and calibrate the climate hydrological models. Then, distributed-GIS-based SCS-CN model was applied to simulate the maximum probable surface runoff for 2000, 2010, and 2020. The comparison results showed that first, deforestation and urbanization have occurred upon the given time and it is anticipated to increase as well. Second, the amount of rainfall has been nonstationary declined till 2015 and this trend is estimated to continue till 2020. Third, due to the damaging changes in LULC and climate, the surface runoff has also increased till 2010 and it is forecasted to gradually exceed

Synergistic use of particle swarm optimization, artificial neural network, and extreme gradient boosting algorithms for urban LULC mapping from WorldView-3 images

Geographic object-based image analysis (GEOBIA) has emerged as an effective and evolving paradigm for analyzing very high resolution (VHR) images as it demonstrates preeminence over the traditional pixel-wise methods and enables the utilization of diverse spectral, geometrical, and textural information to for image classification. Among feature selection (FS) methods, metaheuristic FS techniques have recently demonstrated effective performance in the dimensionality reduction of GEOBIA features. In this study, an artificial neural network (ANN) was integrated with particle swarm optimization (PSO) to enhance the learning process and more effectively determine the most significant features and their importance using WorldView-3 (WV-3) satellite data. First, multi-resolution image segmentation parameters were tuned using Taguchi optimization technique and unsupervised segmentation quality measure. Second, the proposed ANN–PSO was compared with PSO under 100 iterations. The ANN–PSO integration achieved lower root mean square error (RMSE) in all the iterations. Third, state-of-the-art extreme gradient boosting (Xgboost) image classifier was used to derive the land use/land cover (LULC) map of the first study area and assess the transferability of the selected features on the second and third regions. The Xgboost classifier obtained 91.68%, 89.54%, and 89.33% overall accuracies for the first, second, and third sites, respectively. ANN contributed to an intelligent approach for identifying which features are more likely to be relevant and discriminate the land cover types. The proposed integrated FS is a promising approach and an efficient tool for determining significant features and enhancing the detection of urban LULC classes from WV-3 data