Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method

Semiautomated object-based classification of rain-induced landslides with VHR multispectral images on Madeira Island

“…The approach combines object-based image analysis and an SVM supervised learning algorithm, and it was tested with a GeoEye-1 multispectral image (0.5 m panchromatic band), sensed 3 days after widespread landslides and flash floods on Madeira Island, using as ancillary data a pre-event high-resolution (4 m) lidar DTM. Our study confirms the high suitability of VHR multispectral images for landslide mapping (Van Westen et al, 2008), in particular with semiautomated methods, expanding the number of applications that target single post-event optical images (e.g., Cheng et al, 2013;Moosavi et al, 2014). In our study area, this choice proved much less demanding in terms of pre- processing than the change detection approaches more common in the literature (e.g., Lu et al, 2011;Mondini et al, 2011a, b), for which it is especially important to pay attention to co-registration and radiometric correction (Guzzetti et al, 2012).…”

“…Our results also broaden the growing number of applications of object-based image analysis (OBIA) for automated landslide mapping, exemplifying the advantage of also using spatial and textural features in the classification and not only spectral attributes as in the pixel-based approach. Very few previous landslide studies have integrated OBIA and SVMs (Van den Eeckhaut et al, 2012;Moosavi et al, 2014), and we confirm that this is a robust and efficient approach, able to detect 95 % of the number of landslides scars present in the validation areas. Also, to our knowledge, very few previous studies have used OBIA to tackle the problem of automated mapping of landslide source and run-out areas in optical images: Holbling et al (2015) have recently tested such an approach in northern Taiwan, but their reference data set excluded debris flows or other sediment transport areas.…”

“…In recent years, several semiautomated methods have been developed to tackle such difficulties, using specific classification schemes that target single post-event optical images (Cheng et al, 2013;Moosavi et al, 2014) or, when suitable pre-event data are available, exploit pre-and post-event image changes (e.g., Lu et al, 2011;Mondini et al, 2011a, b). In the latter case, great care has to be taken in the coregistration and radiometric correction procedures.…”

“…The "object-oriented" approach, however, groups image pixels into homogeneous objects, with shape, size, neighboring, and textural features in addition to spectral information (Aksoy et al, 2012). With both approaches, supervised and unsupervised classification schemes have been adopted, based on algorithms such as maximum likelihood (Nichol et al, 2005;Borghuis et al, 2007;Danneels et al, 2007), K nearest neighbor (Cheng et al, 2013;Li et al, 2013), artificial neural networks (Nichol et al, 2005;Danneels et al, 2007;Moosavi et al, 2014), random forests , or support vector machines (SVMs; Pisani et al, 2012;Van Den Eeckhaut et al, 2012;Moosavi et al, 2014). Novel object-based approaches for automated landslide mapping include the classification of different landslide types (Martha et al, 2010), identification of landslides from panchromatic imagery only through strong reliance on texture measures , or the detection and mapping of forested landslides resorting to lidar data (Van Den Eeckhaut et al, 2012).…”

“…SVMs are gaining popularity in the remote sensing field, including landslide mapping (Van Den Eeckhaut et al, 2012;Moosavi et al, 2014), due to their ability to handle data with unknown statistical distributions and small training sets, as is often the case in this field (Mountrakis et al, 2011). SVMs are binary classifiers whose aim is to find the decision region boundary that separates the data set characteristics or features into two regions in the feature space.…”