Document Type : Original Article

Authors

1 M.Sc. Student, Dep. of Remote Sensing and GIS, Tarbiat Modares University, Tehran

2 Assistant Prof., Dep. of Remote Sensing and GIS, Tarbiat Modares University, Tehran

3 Assistant Prof., Dep. of Remote Sensing and GIS, Shahid Beheshti University, Tehran

Abstract

It is necessary to know about the quantity of urban tree canopy cover due to its role in air and noise pollution reduction, wind prevention, saving rain water, and runoff control. Being expensive and time consuming, the manual extraction of tree canopy has been replaced by remote sensing techniques conducted on the images, digitally. There are several parameters which must be optimized prior to use of object oriented classification. One of these parameters is Scale affecting the segmentation results, significantly. Scale is usually set by trial and error which is an experimental approach. One of the aims of this study is to optimize Scale parameter, automatically. In addition, after segmentation process based on a proper Scale, it is required to classify the identified segments based on the attributes which are extracted from these segments. In this stage, the selection of suitable classification method fed by the proper attributes is critical. In this research, LiDAR data and aerial image acquired on Vaihingen, Germany, were utilized for segmenting the urban area. In order to identify suitable attributes, random forest feature selection was applied on the attributes derived from the identified segments. Machine learning methods including support vector machine, random forest, and decision tree were compared for classifying the segments based on their suitable attributes into two classes including tree canopy cover and others. The results indicated that Scale of 25 is the best one to segment this area. Also, the tree canopy cover map derived from support vector machine with quality index of 79.90 showed the best performance among different classifiers used in this study.

Keywords

Agarwal, S., Vailshery, L., Jaganmohan, M. & Nagendra, H., 2013, Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches, ISPRS International Journal of Geo-Information, 2(1), PP. 220-236.
Baatz, M., 2000, Multi Resolution Segmentation: An Optimum Approach for High Quality Multi Scale Image Segmentation. Paper presented at the Beutrage zum AGIT-Symposium. Salzburg, Heidelberg, 2000.
Bekkari, A., Idbraim, S., Elhassouny, A., Mammass, D., El Yassa, M. & Ducrot, D., 2012, Spectral and Spatial Classification of High Resolution Urban Satellites Images Using Haralick Features and SVM with SAM and EMD Distance Metrics, International Journal of Computer Applications, 46(11), PP. 28-37.
Breiman, L., 1996, Bagging Predictors, Machine learning, 24(2), PP. 123-140.
Breiman, L., 2001, Random Forests, Machine Learning, 45(1), PP. 5-32.
Cai, L., Shi, W., Miao, Z. & Hao, M., 2018, Accuracy Assessment Measures for Object Extraction from Remote Sensing Images, Remote Sensing, 10(2), P. 303.
Camps-Valls, G. & Bruzzone, L., 2005, Kernel-based Methods for Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, 43(6), PP. 1351-1362.
Chen, M., Su, W., Li, L., Zhang, C., Yue, A. & Li, H., 2009, Comparison of Pixel-Based and Object-Oriented Knowledge-Based Classification Methods Using SPOT5 Imagery, WSEAS Transactions on Information Science and Applications, 3(6), PP. 477-489.
Cortes, C. & Vapnik, V., 1995, Support-Vector Networks, Machine Learning, 20(3), PP. 273-297.
Drǎguţ, L., Tiede, D. & Levick, S.R., 2010, ESP: A Tool to Estimate Scale Parameter for Multiresolution Image Segmentation of Remotely Sensed Data, International Journal of Geographical Information Science, 24(6), PP. 859-871.
Friedl, M. A. & Brodley, C.E., 1997, Decision Tree Classification of Land Cover from Remotely Sensed Data, Remote Sensing of Environment, 61(3), PP. 399-409.
Gerke, M. & Xiao, J., 2014, Fusion of Airborne Laserscanning Point Clouds and Images for Supervised and Unsupervised Scene Classification, ISPRS Journal of Photogrammetry and Remote Sensing, 87, PP. 78-92.
Heipke, C., Mayer, H., Wiedemann, C. & Jamet, O., 1997, Evaluation of Automatic Road Extraction, International Archives of Photogrammetry and Remote Sensing, 32(3 SECT 4W2), PP. 151-160.
Kavzoglu, T. & Tonbul, H., 2018, An Experimental Comparison of Multi-Resolution Segmentation, SLIC and K-Means Clustering for Object-Based Classification of VHR Imagery, International Journal of Remote Sensing, 39(18), PP. 6020-6036.
Kim, M., Madden, M. & Warner, T. 2008, Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery, Object-Based Image Analysis (PP. 291-307), Springer.
 
Liu, D. & Xia, F., 2010, Assessing Object-Based Classification: Advantages and Limitations, Remote Sensing Letters, 1(4), PP. 187-194.
MacFaden, S.W., O'Neil-Dunne, J.P., Royar, A.R., Lu, J.W. & Rundle, A.G., 2012, High-Resolution Tree Canopy Mapping for New York City Using LIDAR and Object-Based Image Analysis, Journal of Applied Remote Sensing, 6(1), P. 063567.
Mallet, C., Bretar, F., Roux, M., Soergel, U. & Heipke, C., 2011, Relevance Assessment of Full-Waveform Lidar Data for Urban Area Classification, ISPRS Journal of Photogrammetry and Remote Sensing, 66(6), PP. S71-S84.
Mathieu, R. & Aryal, J., 2005, Object-Oriented Classification and Ikonos Multispectral Imagery for Mapping Vegetation Communities in Urban Areas, 17th Annual Colloquium of the Spatial Information Research Centre (SIRC 2005: A Spatio-temporal Workshop), Dunedin, New Zealand.
Melgani, F. & Bruzzone, L., 2004, Classification of Hyperspectral Remote Sensing Images with Support Vector Machines, IEEE Transactions on Geoscience and Remote Sensing, 42(8), PP. 1778-1790.
Moussa, A. & El-Sheimy, N., 2012, A New Object Based Method for Automated Extraction of Urban Objects from Airborne Sensors Data, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 39, P. B3.
Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S. & Weng, Q., 2011, Per-Pixel vs. Object-Based Classification of Urban Land Cover Extraction Using High Spatial Resolution Imagery, Remote Sensing of Environment, 115(5), PP. 1145-1161.
Niemeyer, J., Rottensteiner, F. & Soergel, U., 2013, Classification of Urban LiDAR Data Using Conditional Random Field and Random Forests, Paper presented at the Joint Urban Remote Sensing Event 2013.
Nitze, I., Schulthess, U. & Asche, H., 2012, Comparison of Machine Learning Algorithms Random Forest, Artificial Neural Network and Support Vector Machine to Maximum Likelihood for Supervised Crop Type Classification, Proc. of the 4th GEOBIA, P. 35.
Parmehr, E.G., Amati, M., Taylor, E.J. & Livesley, S.J., 2016, Estimation of Urban Tree Canopy Cover Using Random Point Sampling and Remote Sensing Methods, Urban Forestry & Urban Greening, 20, PP. 160-171.
Rahman, M. R., & Saha, S. K. (2008). Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data. Journal of the Indian Society of Remote Sensing, 36(2), 189-201.
Safavian, S.R. & Landgrebe, D., 1991, A Survey of Decision Tree Classifier Methodology, IEEE Transactions on Systems, Man, and Cybernetics, 21(3), PP. 660-674.
Shaban, M. & Dikshit, O., 2001, Improvement of Classification in Urban Areas by the Use of Textural Features: The Case Study of Lucknow City, Uttar Pradesh, International Journal of Remote Sensing, 22(4), PP. 565-593.
Shao, Y. & Lunetta, R.S., 2012, Comparison of Support Vector Machine, Neural Network, and CART Algorithms for the Land-Cover Classification Using Limited Training Data Points, ISPRS Journal of Photogrammetry and Remote Sensing, 70, PP. 78-87.
Timofeev, R., 2004, Classification and Regression Trees (CART) Theory and Applications, Humboldt University, Berlin.
Van der Linden, S., Rabe, A., Okujeni, A. & Hostert, P., 2009, Image SVM Classification, Application Manual: Image SVM Version, 2.
Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J. & Atkinson, P.M., 2018, An Object-Based Convolutional Neural Network (OCNN) for Urban Land Use Classification, Remote sensing of environment, 216, PP. 57-70.
Zhao, W., Du, S. & Emery, W.J., 2017, Object-Based Convolutional Neural Network for High-Resolution Imagery Classification, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(7), PP. 3386-3396.