Abstract
Landslides are one of the severe natural catastrophes that affect thousands of lives and cause colossal damage to infrastructure from small to region scales. Detection of landslide is a prerequisite for damage assessment. We propose a novel method based on object-oriented image analysis using bi-temporal satellite images and DEM. The proposed methodology involves segmentation, followed by extraction of spatial and spectral features of landslides and classification based on supervised Bayesian classifier. The proposed framework is based on the change detection of spatial features which capture the spatial attributes of landslides. The proposed methodology has been applied for the detection and mapping of landslides of different sizes in selected study sites in Himachal Pradesh and Uttarakhand, India. For this, high-resolution multispectral images from the IRS, LISS-IV sensor and DEM from Cartosat-1 are used in this study. The resultant landslides are compared and validated with the inventory landslide maps. The results show that the proposed methodology can identify medium- and large-scale landslides efficiently.
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References
Cruden, David M.:A simple definition of a landslide. Bulletin of Engineering Geology and the Environment 43.1, 27–29(1991).
Gorum, Tolga, et al.:Distribution pattern of earthquake-induced landslides triggered by the 12 May 2008 Wenchuan earthquake, Geomorphology 133.3, 152–167(2011).
Veena, VS and Sai, Subrahmanyam Gorthi and Tapas, Ranjan Martha and Deepak, Mishra and Rama, Rao Nidamanuri.:Automatic detection of landslides in object-based environment using open source tools(2016).
Martha, Tapas R and Kerle, Norman and Jetten, Victor and van Westen, Cees J and Kumar, K Vinod: Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology. 116. Elsevier. 78, 24–36(2010).
Li, Zhongbin and Shi, Wenzhong and Lu, Ping and Yan, Lin and Wang, Qunming and Miao, Zelang: Landslide mapping from aerial photographs using change detection-based Markov random field. Remote Sensing of Environment, 187, 76–90(2016).
Heleno, Sandra and Matias, Magda and Pina, Pedro and Sousa, António Jorge.: Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island, Nat. Hazards Earth Syst. Sci, 16, 1035–1048(2016).
Chang, Li-Wei, Pi-Fuei Hsieh, and Ching-Weei Lin.:Landslide identification based on FORMOSAT-2 multispectral imagery by wavelet-based texture feature extraction. Geoscience and Remote Sensing Symposium(2006).
McCallum, Andrew and Nigam, Kamal and others.:A comparison of event models for naive bayes text classification, AAAI-98 workshop on learning for text categorization, 752, 41–48(1998).
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Madalasa, P., Sai Subrahmanyam, G.R.K., Martha, T.R., Nidamanuri, R.R., Mishra, D. (2018). Bayesian Approach for Landslide Identification from High-Resolution Satellite Images. In: Chaudhuri, B., Kankanhalli, M., Raman, B. (eds) Proceedings of 2nd International Conference on Computer Vision & Image Processing . Advances in Intelligent Systems and Computing, vol 704. Springer, Singapore. https://doi.org/10.1007/978-981-10-7898-9_2
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DOI: https://doi.org/10.1007/978-981-10-7898-9_2
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