Selection of optimal pixel resolution for landslide susceptibility analysis within the Bukit Antarabangsa, Kuala Lumpur, by using image processing and multivariate statistical tools

Quraishi et al. EURASIP Journal on Image and Video Processing (2017) 2017:21 DOI 10.1186/s13640-017-0169-2 EURASIP Journal on Image and Video Processing RESEARCH Open Access Selection of optimal pixel resolution for landslide susceptibility analysis within the Bukit Antarabangsa, Kuala Lumpur, by using image processing and multivariate statistical tools Iqbal Quraishi1*, Abul Hasnat2 and J. Paul Choudhury1 Abstract Landslides are considered as one of the natural hazards responsible for casualties, damage of assets, and infrastructures. In many situations, collection of field data from remote places is difficult due to inaccessibility of landslide area. This paper examines landslide susceptibility in the Bukit Antarabangsa, Kuala Lumpur, to ease geographical studies, using image processing and multivariate statistical tools by reviewing the digital images using remote-sensing technique without any physical survey. We considered different pixel resolutions and report the effectiveness of using factor analysis, principal component analysis, linear discriminant analysis, and their hybridization. Eight types of databases for heavy, medium, and no landslide were created. The modeling works were carried out at 2 × 2, 4 × 4, 8 × 8, 16 × 16, 32 × 32, 64 × 64, 128 × 128, and 256 × 256 pixel resolutions. Results indicate 2 × 2 was optimal in both heavy and medium while 8 × 8 found to be ideal for no landslide region. Performance at different pixel resolutions was compared using receiver operating characteristic (ROC) curves, and average success of 87.36% was found. This simple yet robust system holds great potential for saving lives. Keywords: Landslide, Image processing, Factor analysis, Principal component analysis, Linear discriminant analysis, Residual analysis, Multivariate statistical tools 1 Introduction Landslide is an extreme natural phenomenon that takes a heavy toll on human life and property leaving farreaching consequence not only on economy but also nature and ecosystem of the affected region. Flash flood, long and terrible monsoon, earthquake rock sliding or toppling, soil cave-in, and sudden profusion of snow melting are some of the elementals that precede landslide in a particular area. Again, soil condition alters after earthquake or any other natural transformation in a region and its neighborhood making it more vulnerable to landslide. Landslide susceptibility analysis can be helpful in such case as certain preventive measures can * Correspondence: 1 Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia, West Bengal, India Full list of author information is available at the end of the article be taken in time to minimize future threat to human life in the best possible way. Tarantino et al. [1] had applied change detection techniques for monitoring landslides in southern Italy. Saha et al. [2] had utilized geographic information system (GIS)-based statistical approach for landslide susceptibility in the Himalayas. Liang et al. [3] had used multi-satellite images and GIS data for statistical analysis of landslide in Taiwan Island. Rau et al. [4] had applied time series satellite images for monitoring and assessment of landslide. Voigt et al. [5] had shown the efficient use of image analysis based on satellite images for landslide mapping. Joyce et al. [6] had also used image processing techniques with manual interpretation for predicting landslide proneness. Rainfall recording data had been taken by Martelloni et al. [7] for the prediction of landslide in a local territory of Emilia Romagna, Italy. Kanungo and Sharma [8] had © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Quraishi et al. EURASIP Journal on Image and Video Processing (2017) 2017:21 Page 2 of 12 Table 2 Image features and their description Feature No. of Features Details Fractal dimension 3 Mean of fractal dimension, standard deviation of fractal dimension, lacunarity Ripplet coefficient 2 Mean of ripplet coefficient, standard deviation of ripplet coefficient Autocorrelation coefficient 2 Mean of autocorrelation coefficient, standard deviation of autocorrelation coefficient Gray level co-occurrence matrix 16 Four set of data values; each set consist of the contrast, homogeneity, energy, and correlation Gray level run length matrix 44 Short run emphasis, long run emphasis, gray level non-uniformity, run length non-uniformity, run percentage, low gray level run emphasis, high gray level run emphasis, short run low gray level emphasis, short run high gray level emphasis, long run low gray level emphasis, long run high gray level emphasis. Each of the above was calculated for 0, 45, 90, and 135 Gabor coefficient 60 Mean and standard deviation of Gabor coefficients for 0, 15, 30, 45, 60, 75 Moments 4 First moment, second moment, third moment, and fourth moment Fig. 1 Block diagram of the proposed system obtained rainfall threshold values for landslides in and around Chamoli, Joshimath region of the Garhwal Himalayas, India. Cultivated, fallow, and wood land data had been investigated by Biro et al. [9] for landslide assessment. Topological, geological, and environmental parameters are used as parameters for landslide assessment by Akgun [10]. Aerial photographs and field surveys of Cameron Highlands, Malaysia, had been utilized by Pradhan et al. [11] to analyze landslide hazards, and accuracy of 83.45% was established. Pradhan et al. [12] have also used spatial-based statistical models for landslide hazard analysis. Pradhan et al. [13] developed a neuro-fuzzy model using remote-sensing data and GIS in a part of the Cameron Highland areas in Malaysia. GIS and remote-sensing data were used by Lee et al. [14, 15] for the assessment of landslide. Historical data of rainfall and earthquake was taken by Muthu et al. [16] for landslide analysis. Assessment of landslide using lithology, rock weathering, geomorphology, soil type, and depth had been conducted by Champatiray et al. [17]. It is observed that most of the authors have used field data like Table 1 Pixel resolution and their dimensions geological and topological parameters, rainfall, and their likes in addition to the satellite image information of that place for the assessment of landslide susceptibility. The objective of our research work is to analyze and asses the landslide susceptibility based on the examination of the satellite images of a particular region. It must be borne in mind the image feature values of a landslide-affected area are different from those of the Table 3 Multivariate statist (...truncated)