Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloLandslide mapping from multi-sensor data through improved change detection-based Markov random field
Anno di pubblicazione2019
Autore/iPing Lu Yuanyuan Qin Zhongbin Li Alessandro C. Mondini NicolaCasagli
Affiliazioni autoriCollege of Surveying and Geo-Informatics, Tongji University, Siping Road, 1239, Shanghai, China Center for Global Change and Earth Observations, Michigan State University, East Lansing, MI, USA CNR IRPI, Via Madonna Alta 126, Perugia, Italy Department of Earth Sciences, University of Firenze, Via La Pira 4, Firenze, Italy
Autori CNR e affiliazioni
  • inglese
AbstractAccurate landslide inventory mapping is essential for quantitative hazard and risk assessment. Although multi-temporal change detection techniques have contributed greatly to landslide inventory preparation, it is still challenging to generate quality change detection images (CDIs) for accurate landslide mapping. The recently proposed change detection-based Markov random field (CDMRF) provides an effective approach for rapid mapping of landslides with minimum user interventions. However, when CDI is generated by change vector analysis (CVA) alone, the CDMRF method may suffer from noise especially when the pre- and post-event remote sensing images are acquired under different atmospheric, illumination, and phenological conditions. This paper improved such CDMRF approach by integrating normalized difference vegetation index (NDVI), principal component analysis (PCA), and independent component analysis (ICA) generated CDIs with MRF for landslide inventory mapping from multi-sensor data. To justify the effectiveness and applicability, the improved methods were applied to map rainfall-, typhoon-, and earthquake-triggered landslides from the pre- and post-event satellite images acquired by very high resolution QuickBird, high resolution FORMOSAT-2, and moderate resolution Sentinel-2. Moreover, they were tested on pre-event Landsat-8 and post-event Sentinel-2 datasets, indicating that they are operational for landslide inventory mapping from combined multi-temporal and multi-sensor data. The results demonstrate that the improved ?NDVI-, PCA-, and ICA-based approaches perform much better than CVA-based CDMRF in terms of completeness, correctness, Kappa coefficient, and F-measures. To the best of our knowledge, it is the first time that NDVI, PCA, and ICA are integrated with MRF for landslide inventory mapping from multi-sensor data. It is anticipated that this research can be a starting point for developing new change detection techniques that can readily generate quality CDI and for applying advanced machine learning algorithms (e.g., deep learning) to automatic detection of natural hazards from multi-sensor time series data.
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RivistaRemote sensing of environment
Attiva dal 1969
Editore: American Elsevier Pub. Co., - New York,
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 0034-4257
Titolo chiave: Remote sensing of environment
Titolo proprio: Remote sensing of environment.
Titolo abbreviato: Remote sens. environ.
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Stato della pubblicazionePublished version
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Parole chiaveLandslide inventory mapping Change detection NDVI Principal component analysis Independent component analysis Markov random field (MRF) Multi-sensor
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Strutture CNR
  • IRPI — Istituto di ricerca per la protezione idrogeologica
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Progetti Europei-