FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2954332586> ?p ?o ?g. }

• W2954332586 endingPage "111235" @default.
• W2954332586 startingPage "111235" @default.
• W2954332586 abstract "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 δNDVI-, 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." @default.
• W2954332586 created "2019-07-12" @default.
• W2954332586 creator A5005401772 @default.
• W2954332586 creator A5012274844 @default.
• W2954332586 creator A5024006857 @default.
• W2954332586 creator A5040751828 @default.
• W2954332586 creator A5063581633 @default.
• W2954332586 date "2019-09-01" @default.
• W2954332586 modified "2023-10-13" @default.
• W2954332586 title "Landslide mapping from multi-sensor data through improved change detection-based Markov random field" @default.
• W2954332586 cites W1929725744 @default.
• W2954332586 cites W1964194978 @default.
• W2954332586 cites W1964262728 @default.
• W2954332586 cites W1968128178 @default.
• W2954332586 cites W1968353677 @default.
• W2954332586 cites W1970331803 @default.
• W2954332586 cites W1974287654 @default.
• W2954332586 cites W1974388905 @default.
• W2954332586 cites W1975382649 @default.
• W2954332586 cites W1984285105 @default.
• W2954332586 cites W1985245433 @default.
• W2954332586 cites W1995085431 @default.
• W2954332586 cites W2001645642 @default.
• W2954332586 cites W2012089683 @default.
• W2954332586 cites W2015619014 @default.
• W2954332586 cites W2016016500 @default.
• W2954332586 cites W2016167483 @default.
• W2954332586 cites W2023655740 @default.
• W2954332586 cites W2023819943 @default.
• W2954332586 cites W2024756413 @default.
• W2954332586 cites W2024976610 @default.
• W2954332586 cites W2026262541 @default.
• W2954332586 cites W2028977086 @default.
• W2954332586 cites W2034516548 @default.
• W2954332586 cites W2036630433 @default.
• W2954332586 cites W2036881582 @default.
• W2954332586 cites W2037028349 @default.
• W2954332586 cites W2044907109 @default.
• W2954332586 cites W2054348793 @default.
• W2954332586 cites W2054512946 @default.
• W2954332586 cites W2058082754 @default.
• W2954332586 cites W2058436841 @default.
• W2954332586 cites W2060382151 @default.
• W2954332586 cites W2065227967 @default.
• W2954332586 cites W2069677644 @default.
• W2954332586 cites W2070970068 @default.
• W2954332586 cites W2080134555 @default.
• W2954332586 cites W2081620141 @default.
• W2954332586 cites W2085991257 @default.
• W2954332586 cites W2089314377 @default.
• W2954332586 cites W2095057310 @default.
• W2954332586 cites W2107884096 @default.
• W2954332586 cites W2113137767 @default.
• W2954332586 cites W2113972550 @default.
• W2954332586 cites W2119300483 @default.
• W2954332586 cites W2122998546 @default.
• W2954332586 cites W2123649031 @default.
• W2954332586 cites W2124351162 @default.
• W2954332586 cites W2124797185 @default.
• W2954332586 cites W2128728535 @default.
• W2954332586 cites W2134702142 @default.
• W2954332586 cites W2139596835 @default.
• W2954332586 cites W2141422564 @default.
• W2954332586 cites W2142347478 @default.
• W2954332586 cites W2150093133 @default.
• W2954332586 cites W2152273606 @default.
• W2954332586 cites W2155789665 @default.
• W2954332586 cites W2165577558 @default.
• W2954332586 cites W2233053992 @default.
• W2954332586 cites W2273251202 @default.
• W2954332586 cites W2507470144 @default.
• W2954332586 cites W2509507403 @default.
• W2954332586 cites W2517757032 @default.
• W2954332586 cites W2530415363 @default.
• W2954332586 cites W2556865753 @default.
• W2954332586 cites W2649407104 @default.
• W2954332586 cites W2736022141 @default.
• W2954332586 cites W2742264176 @default.
• W2954332586 cites W2753093605 @default.
• W2954332586 cites W2788032251 @default.
• W2954332586 cites W2792546905 @default.
• W2954332586 cites W2801886117 @default.
• W2954332586 cites W2896624250 @default.
• W2954332586 cites W2934708281 @default.
• W2954332586 cites W834961641 @default.
• W2954332586 doi "https://doi.org/10.1016/j.rse.2019.111235" @default.
• W2954332586 hasPublicationYear "2019" @default.
• W2954332586 type Work @default.
• W2954332586 sameAs 2954332586 @default.
• W2954332586 citedByCount "93" @default.
• W2954332586 countsByYear W29543325862019 @default.
• W2954332586 countsByYear W29543325862020 @default.
• W2954332586 countsByYear W29543325862021 @default.
• W2954332586 countsByYear W29543325862022 @default.
• W2954332586 countsByYear W29543325862023 @default.
• W2954332586 crossrefType "journal-article" @default.
• W2954332586 hasAuthorship W2954332586A5005401772 @default.
• W2954332586 hasAuthorship W2954332586A5012274844 @default.

• next