Document Type : علمی - پژوهشی
Authors
1 M.Sc. Student, Faculty of Geodesy & Geomatics Engineering, K.N. Toosi University of Technology
2 Associate Prof. of Remote Sensing Research Center, Faculty of Geodesy & Geomatics Engineering, K.N. Toosi University of Technology
3 Assistant Prof. of Remote Sensing Research Center, Faculty of Geodesy & Geomatics Engineering, K.N. Toosi University of Technology
Abstract
Determination of land use and land cover changes is necessary for monitoring of urban growth and responsible urban planning. Remote sensing can be used as a powerful technology in land use and land cover change detection. One of the challenges in this area is to developing efficient methods for accurate and highly automated change detection which can produce accurate and precise information about position and content of the changes. In this study two GeoEye images from Tehran 17th region related to 2004 and 2010 years were used. This study Proposed a method based on image context spatial features, neural networks and genetic algorithm. Six cases with direct multi-date classification approach and post classification approach were implemented and compared in the viewpoints of accuracy and runtime. Direct multi-date classification was superior in all six cases. Between six implemented cases, sixth case (proposed method of this research) was superior in the classification accuracy point of view. In this case after selecting optimized features, ANN classification was executed based on determining architecture and several times execution. Though runtime of sixth case was the highest, if accuracy is prior, it’s highly recommended.
Keywords
- Benediktsson, J.A., Swain P.H. & Ersoy O.K., 1990, Neural Network Approaches versus Statistical Methods in Classification of Multisource Remote Sensing Data, Geoscience and Remote Sensing, IEEE Transactions on 28(4), PP. 540-552.
- Bruzzone, L. & Serpico, S.B., 1997, An Iterative Technique for the Detection of Land-cover Transitions in Multitemporal Remote-sensing Images, Geoscience and Remote Sensing, IEEE Transactions on 35(4), PP. 858-867.
- Chen, C., Chen, K.S. & Chang, J., 1995, Neural Network for Change Detection of Remotely Sensed Imagery.
- Chini, M., Pacifici, F., Emery, W.J., Pierdicca, N. & Del Frate, F., 2008, Comparing Statistical and Neural Network Methods Applied to very high Resolution Satellite Images Showing Changes in Man-made Structures at Rocky Flats, Geoscience and Remote Sensing, IEEE Transactions on 46(6), PP. 1812-1821.
- Coppin, P.R. & Bauer, M.E., 1996, Digital Change Detection in Forest Ecosystems with Remote Sensing Imagery, Remote Sensing Reviews 13(3), PP. 207-234.
- Dai, X. & Khorram, S., 1998, The Effects of Image Misregistration on the Accuracy of Remotely Sensed Change Detection, Geoscience and Remote Sensing, IEEE Transactions on 36(5), PP. 1566-1577.
- Haralick, R.M., Shanmugam K. & Dinstein I.H., 1973, Textural Features for Image Classification, Systems, Man and Cybernetics, IEEE Transactions on(6),
- PP. 610-621.
- Heermann, P.D. & Khazenie N., 1992, Classification of Multispectral Remote Sensing Data using a Back-propagation Neural Network, Geoscience and Remote Sensing, IEEE Transactions on 30(1),
- PP. 81-88.
- Hertz, J., Krogh A. & Palmer, R.G., 1991, Introduction to the Theory of Neural Computation, Westview press.
- Kabir, S., He, D. & Hussina, W., 2010, Texture Analysis of IKONOS Satellite Imagery for Urban Land use and Land Cover Classification, Imaging Science Journal, 58(3), PP. 163-170.
- Liu, Y., Starzyk J.A. & Zhu Z., 2007, Optimizing Number of Hidden Neurons in Neural Networks, Artificial Intelligence and Applications.
- Long, D., 1999, Remotely Sensed Change Detection based on Artificial Neural Networks, Photogrammetric engineering and remote sensing, 65(10), PP. 1187-1194.
- Neagoe, V.E., Neghina, M. & Datcu, M., 2012, Neural Network Techniques for Automated Land-Cover Change Detection in Multispectral Satellite Time Series Imagery, International Journal of Mathematical Models and Methods in Applied Sciences, 6(1), PP. 130-139.
- Nguyen, D. & Widrow, B., 1990, Improving the Learning Speed of 2-layer Neural Networks by Choosing Initial Values of the Adaptive Weights, Neural Networks, 1990 IJCNN International Joint Conference on, IEEE.
- Pacifici, F., Del Frate, F., Solimini, C. & Emery, W.J., 2007, An Innovative Neural-net Method to Detect Temporal Changes in High-resolution Optical Satellite Imagery, Geoscience and Remote Sensing, IEEE Transactions on 45(9), PP. 2940-2952.
- Paola, J. & Schowengerdt R., 1995, A Review and Analysis of Backpropagation Neural Networks for Classification of Remotely-sensed Multi-spectral Imagery, International Journal of Remote Sensing, 16, PP. 3033-3033.
- Paola, J.D. & Schowengerdt, R.A., 1995, A Detailed Comparison of Backpropagation Neural Network and Maximum-likelihood Classifiers for Urban Land Use Classification, Geoscience and Remote Sensing, IEEE Transactions on 33(4),
- PP. 981-996.
- Pu, R., Gong, P., Tian, Y., Miao, X., Carruthers, R.I. & Anderson G.L., 2008, Invasive Species Change Detection using Artificial Neural Networks and CASI Hyperspectral Imagery, Environmental monitoring and assessment, 140(1-3), PP. 5-32.
- Richards, J.A. & Jia, X., 2006, Remote Sensing Digital Image Analysis: an introduction, Springer Verlag.
- Rumelhart, D.E., Hintont G.E. & Williams, R.J., 1986, Learning Representations by Back-propagating Errors, Nature 323(6088),
- PP. 533-536.
- Zhang, Y., 1999, Optimisation of Building Detection in Satellite Images by Combining Multispectral Classification and Texture Filtering, ISPRS journal of photogrammetry and remote sensing, 54(1), PP. 50-60.
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