Object-Oriented Change Detection for Landslide Rapid Mapping
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- 10 February 2011
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 8 (4), 701-705
- https://doi.org/10.1109/lgrs.2010.2101045
Abstract
A complete multitemporal landslide inventory, ideally updated after each major event, is essential for quantitative landslide hazard assessment. However, traditional mapping methods, which rely on manual interpretation of aerial photographs and intensive field surveys, are time consuming and not efficient for generating such event-based inventories. In this letter, a semi-automatic approach based on object-oriented change detection for landslide rapid mapping and using very high resolution optical images is introduced. The usefulness of this methodology is demonstrated on the Messina landslide event in southern Italy that occurred on October 1, 2009. The algorithm was first developed in a training area of Altolia and subsequently tested without modifications in an independent area of Itala. Correctly detected were 198 newly triggered landslides, with user accuracies of 81.8% for the number of landslides and 75.9% for the extent of landslides. The principal novelties of this letter are as follows: 1) a fully automatic problem-specified multiscale optimization for image segmentation and 2) a multitemporal analysis at object level with several systemized spectral and textural measurements.Keywords
This publication has 23 references indexed in Scilit:
- Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modelingComputers & Geosciences, 2009
- PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite dataInternational Journal of Remote Sensing, 2008
- Improvement of Image Segmentation Accuracy Based on Multiscale Optimization ProcedureIEEE Geoscience and Remote Sensing Letters, 2008
- Quantitative assessment of landslide susceptibility using high‐resolution remote sensing data and a generalized additive modelInternational Journal of Remote Sensing, 2008
- Parameter selection for region‐growing image segmentation algorithms using spatial autocorrelationInternational Journal of Remote Sensing, 2006
- High Spatial Resolution Satellite Imagery, DEM Derivatives, and Image Segmentation for the Detection of Mass Wasting ProcessesPhotogrammetric Engineering & Remote Sensing, 2006
- Satellite remote sensing for detailed landslide inventories using change detection and image fusionInternational Journal of Remote Sensing, 2005
- Classification of soil‐ and bedrock‐dominated landslides in British Columbia using segmentation of satellite imagery and DEM dataInternational Journal of Remote Sensing, 2005
- A comparison of three image-object methods for the multiscale analysis of landscape structureISPRS Journal of Photogrammetry and Remote Sensing, 2003
- Detecting translational landslide scars using segmentation of Landsat ETM+ and DEM data in the northern Cascade Mountains, British ColumbiaCanadian Journal of Remote Sensing, 2003