Landslide mapping from multi-sensor data through improved change detection-based Markov random field

REMOTE SENSING OF ENVIRONMENT

卷:231

Article

Lu, Ping¹  Qin, Yuanyuan¹  Li, Zhongbin²  Mondini, Alessandro C.³  Casagli, Nicola⁴ 

[1] Tongji Univ, Coll Surveying & Geoinformat, Siping Rd 1239, Shanghai, Peoples R China

[2] Michigan State Univ, Ctr Global Change & Earth Observat, E Lansing, MI 48824 USA

[3] CNR IRPI, Via Madonna Alta 126, Perugia, Italy

[4] Univ Firenze, Dept Earth Sci, Via La Pira 4, Florence, Italy

关键词: Landslide inventory mapping;    Change detection;    NDVI;    Principal component analysis;    Independent component analysis;    Markov random field (MRF);    Multi-sensor;   

DOI  :  10.1016/j.rse.2019.111235

来源: Elsevier

【 摘 要 】

Accurate 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 delta 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.

【 授权许可】

Free