Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

Cheng, Gong; Guo, Lei; Zhao, Tianyun; Han, Junwei; Li, Huihui; Fang, Jun

doi:10.1080/01431161.2012.705443

Cited by 227 publications

(107 citation statements)

References 35 publications

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“…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%

“…Ideally, pre-event and post-event images should be acquired at the same time of the year and with similar view angle and solar illumination, but this is often not feasible (Guzzetti et al, 2012). Semiautomated approaches to landslide mapping can be classed, according to the type of image element used, as "pixel based" (e.g., Chang et al, 2007;Yang and Chen, 2010;Chini et al, 2011;Cheng et al, 2013;Mondini et al, 2013Mondini et al, , 2011a or "object based" (e.g., Aksoy et al, 2012;Holbling et al, 2012Holbling et al, , 2015Lacroix et al, 2013;Lahousse et al, 2011;Lu et al, 2011;Martha et al, 2010Martha et al, , 2011Martha et al, , 2013Stumpf et al, 2011Stumpf et al, , 2014Van Den Eeckhaut et al, 2012). When applied to very high spatial resolution images, pixel-based methods often exhibit a "salt and pepper" appearance (Van Westen et al, 2008;Guzzetti et al, 2012) which requires image post-processing.…”

Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

“…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.…”

Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

See 2 more Smart Citations

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.

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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.

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Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

Section: Automated Methods For Landslide Mappingmentioning

confidence: 99%

See 1 more Smart Citation

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

show abstract

“…Firstly, low-level features, such as spectral, geometrical, and textural image features, are widely used in image analyses [11], but they are weak in characterizing functional zones which are usually composed of diverse objects with variant characteristics [12]. Then, middle-level features, including object semantics [4,8], visual elements [7], and bag-of-visual-word (BOVW) representations [13], are more effective than low-level features in representing functional zones [7], but they ignore spatial and contextual information of objects, leading to inaccurate recognition results. To resolve this issue, Hu et al (2015) extracted high-level features using convolutional neural network (CNN) [10], which could measure contextual information and were more robust than visual features in recognizing functional zones [14,16].…”

Section: Technical Issuesmentioning

confidence: 99%

“…Finally, functional zones can be labeled with categories based on their features. Previous efforts at functional-zone analysis focus mainly on feature representations [9][10][11][12][13][14] and classification methods [4,15,16], but ignore zone segmentation. This is unfortunate because zone segmentation is an essential precursor to the other two steps of functional-zone analysis and is hence fundamental to the entire undertaking.…”

Section: Introductionmentioning

confidence: 99%

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Zhang

Wang

et al. 2018

Remote Sensing

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Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities-Beijing, Putian, and Zhuhai-and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners.

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Automated extraction of mining‐induced ground fissures using deep learning and object‐based image classification

Huangfu,

Qiu,

Cui

et al. 2024

Earth Surf Processes Landf

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Accurate extraction of ground fissures caused by intense coal mining has the potential to significantly improve the efficiency of environmental monitoring in mining areas. However, the extraction results using previous methods have often exhibited issues of discontinuity and substantial deviation from ground truth data, resulting in low extraction accuracy. In this study, a novel approach, ENVINet5‐OBIC, for extracting ground fissures in mining areas is proposed, which integrates object‐based image classification (OBIC) with the pixel‐based deep learning model ENVINet5. ENVINet5‐OBIC uses OBIC to segment high‐resolution unmanned aerial vehicle (UAV) images across different scales, effectively considering shape, texture and correlative information between adjacent pixels. Furthermore, by utilizing homogeneous objects as building blocks, it establishes a deep learning model for the automated extraction of ground fissures. Experimental results show that ENVINet5‐OBIC performs better when compared with OBIC, U‐Net, PSPNet and ENVINet5 methods in terms of continuity, accuracy and error reduction. In addition, the ground fissure area extracted by ENVINet5‐OBIC closely aligns with ground truth data. This study provides a more effective method for automatic extraction of ground fissures, which improves the efficiency of environmental monitoring in mining areas.

show abstract

Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

Cited by 227 publications

References 35 publications

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

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

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Automated extraction of mining‐induced ground fissures using deep learning and object‐based image classification

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