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

Heleno, Sandra; Matias, Magda; Pina, Pedro; Sousa, Ana

doi:10.5194/nhess-16-1035-2016

Cited by 31 publications

(28 citation statements)

References 65 publications

(112 reference statements)

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“…Their method was then tested in the Flemish Ardennes (Belgium) and resulted in landslide extraction accuracies of almost 70%. In another study, [27] used the same ML method, but applied the RBF kernel along with OBIA to propose an automatic landslide extraction approach for rainfall-induced landslides on Madeira Island. Furthermore, [28] integrated the K-means clustering method with both pixel-based and OBIA approaches to compare their performance in landslide detection.…”

Section: Introductionmentioning

confidence: 99%

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

et al. 2019

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Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success.

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Section: Introductionmentioning

confidence: 99%

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

et al. 2019

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“…Landslides have the capability to display heterogeneous sizes that require information with higher spatial resolutions in order to produce complete event inventories [29]. Effective feature selection, such as texture, image band information, and geometric features, are needed to improve the quality of landslide inventory mapping.…”

Section: Introductionmentioning

confidence: 99%

Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster–Shafer

2018

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A detailed and state-of-the-art landslide inventory map including precise landslide location is greatly required for landslide susceptibility, hazard, and risk assessments. Traditional techniques employed for landslide detection in tropical regions include field surveys, synthetic aperture radar techniques, and optical remote sensing. However, these techniques are time consuming and costly. Furthermore, complications arise for the generation of accurate landslide location maps in these regions due to dense vegetation in tropical forests. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) is typically employed to generate accurate landslide maps. The object-based technique generally consists of many homogeneous pixels grouped together in a meaningful way through image segmentation. In this paper, in order to address the limitations of this approach, the final decision is executed using Dempster-Shafer theory (DST) rule combination based on probabilistic output from object-based support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN) classifiers. Therefore, this research proposes an efficient framework by combining three object-based classifiers using the DST method. Consequently, an existing supervised approach (i.e., fuzzy-based segmentation parameter optimizer) was adopted to optimize multiresolution segmentation parameters such as scale, shape, and compactness. Subsequently, a correlation-based feature selection (CFS) algorithm was employed to select the relevant features. Two study sites were selected to implement the method of landslide detection and evaluation of the proposed method (subset "A" for implementation and subset "B" for the transferrable). The DST method performed well in detecting landslide locations in tropical regions such as Malaysia, with potential applications in other similarly vegetated regions.

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“…The Italian test site is divided into two sub-areas located in the Gader Valley. 93.7% for the detection of landslide-affected areas and between 44.8% and 90% for the identification of different landslide types such as debris slides, rock slides, or debris flows [15][16][17][18][19][20][21][22][25][26][27]. Objectbased landslide mapping routines are often tailored to specific study areas and data and thus reveal a lack of transferability and robustness.…”

Section: Study Areasmentioning

confidence: 99%

“…Due to these circumstances, there has been a trend towards object-based landslide recognition in recent years, as demonstrated by a range of studies [8,[16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Object-based image analysis (OBIA) provides a set of innovative tools for semi-automatically delineating and classifying landslides based on EO data.…”

Section: Introductionmentioning

confidence: 99%

Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors

Hölbling

Eisank²,

Albrecht

et al. 2017

Geosciences

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Abstract:Object-based image analysis (OBIA) has been increasingly used to map geohazards such as landslides on optical satellite images. OBIA shows various advantages over traditional image analysis methods due to its potential for considering various properties of segmentation-derived image objects (spectral, spatial, contextual, and textural) for classification. For accurately identifying and mapping landslides, however, visual image interpretation is still the most widely used method. The major question therefore is if semi-automated methods such as OBIA can achieve results of comparable quality in contrast to visual image interpretation. In this paper we apply OBIA for detecting and delineating landslides in five selected study areas in Austria and Italy using optical Earth Observation (EO) data from different sensors (Landsat 7, SPOT-5, WorldView-2/3, and Sentinel-2) and compare the OBIA mapping results to outcomes from visual image interpretation. A detailed evaluation of the mapping results per study area and sensor is performed by a number of spatial accuracy metrics, and the advantages and disadvantages of the two approaches for landslide mapping on optical EO data are discussed. The analyses show that both methods produce similar results, whereby the achieved accuracy values vary between the study areas.

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

Cited by 31 publications

References 65 publications

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster–Shafer

Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors

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