Automatic landslide recognition through Optimum-Path Forest

Pisani, Rodrigo José; Riedel, Paulina Setti; Costa, Kelton Augusto Pontara da; Nakamura, Ricardo; Pereira, Clayton Reginaldo; Rosa, Guilherme Jordão de Magalhães; Papa, João Paulo

doi:10.1109/igarss.2012.6352681

Cited by 6 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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 (Stumpf et al, 2011(Stumpf et al, , 2014, 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).…”

Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

Heleno¹,

Matias²,

Pina³

et al. 2015

Preprint

View full text Add to dashboard Cite

Abstract. A method for semi-automatic landslide detection, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a Support Vector Machine classifier on a GeoEye-1 multispectral image, sensed 3 days after the major damaging landslide event that occurred in Madeira island (20 February 2010), with a pre-event LIDAR Digital Elevation Model. The testing is developed in a 15 km2-wide study area, where 95 % of the landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier east facing-slopes.

show abstract

Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

Heleno¹,

Matias²,

Pina³

et al. 2015

Preprint

View full text Add to dashboard Cite

show abstract

“…Freitas et al [32] estimated rainfall in agricultural areas using GOES satellite weather images, and compared OPF classifiers with SVM, ANN-MLP, and K-NN, with OPF demonstrating a superior runtime. OPF recognized collapsed areas from GeoEye-MS satellite imagery, yielding results similar to cutting-edge techniques [33]. Papa et al [54], proposed a method combining the OPF classifier with three optimization algorithms (PSO, HS, and GSA) to mitigate the problem of reducing hyperspectral image data through band selection.…”

Section: Opf Classifiersmentioning

confidence: 99%

“…However, due to the spatial variability of spectral signatures, the extraction of hyperspectral image characteristics is widely recognized as one of the most challenging tasks in processing hyperspectral images [29] [33], which involves the experience of specialists. Gabor filters [34], adaptive filters [35], and Markov chains [36] are often adopted.…”

Section: Use Of Remote Sensing Data To Identify Geometric Features Ofmentioning

confidence: 99%

Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

Macedo¹,

Maia²,

Rabbani³

et al. 2020

JGIS

View full text Add to dashboard Cite

One of the principal difficulties related to road safety management in Brazil is the lack of data on road projects, especially those on rural roads, which makes it difficult to use road safety studies and models from other countries as a reference. Updating road networks through the use of hyperspectral remote sensing images can be a good alternative. However, accurately recognizing and extracting hyperspectral images from roads has been recognized as a challenging task in the processing of hyperspectral data. In order to solve the aforementioned challenges, Hyperion hyperspectral images were combined with the Optimum Forest Path (OPF) algorithm for supervised classification of rural roads and the effectiveness of the OPF and SVM classifiers when applied to these areas was compared. Both classifiers produced reasonable results, however, the OPF algorithm outperformed SVM. The higher classification accuracy obtained by the OPF was mainly attributed to the ability to better distinguish between regions of exposed soil and unpaved roads.

show abstract

“…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).…”

Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

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

Heleno

Matias

Pina

et al. 2016

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract.A method for semiautomated landslide detection and mapping, with the ability to separate source and run-out areas, is presented in this paper. It combines object-based image analysis and a support vector machine classifier and is tested using a GeoEye-1 multispectral image, sensed 3 days after a major damaging landslide event that occurred on Madeira Island (20 February 2010), and a pre-event lidar digital terrain model. The testing is developed in a 15 km 2 wide study area, where 95 % of the number of landslides scars are detected by this supervised approach. The classifier presents a good performance in the delineation of the overall landslide area, with commission errors below 26 % and omission errors below 24 %. In addition, fair results are achieved in the separation of the source from the run-out landslide areas, although in less illuminated slopes this discrimination is less effective than in sunnier, east-facing slopes.

show abstract

Automatic landslide recognition through Optimum-Path Forest

Cited by 6 publications

References 14 publications

Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

Automated object-based classification of rain-induced landslides with VHR multispectral images in Madeira Island

Remote Sensing Applied to the Extraction of Road Geometric Features Based on Optimum Path Forest Classifiers, Northeastern Brazil

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

Contact Info

Product

Resources

About