3,877
Views
12
CrossRef citations to date
0
Altmetric
Articles

Multi-Regional landslide detection using combined unsupervised and supervised machine learning

, &
Pages 1015-1038 | Received 26 Nov 2020, Accepted 27 Mar 2021, Published online: 19 Apr 2021

References

  • Baraldi A, Puzzolo V, Blonda P, Bruzzone L, Tarantino C. 2006. Automatic spectral rule-based preliminary mapping of calibrated Landsat TM and ETM + images. IEEE Trans Geosci Remote Sensing. 44(9):2563–2586.
  • Bholowalia P, Kumar A. 2014. EBK-means: A clustering technique based on elbow method and k-means in WSN. Int J Comput Appl. :105(9).
  • Biau G, Scornet E. 2016. A random forest guided tour. Test. 25(2):197–227. 2016.
  • Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens . 65(1):2–16.
  • Blaschke T. 2013. Object Based Image Analysis: A new paradigm in remote sensing. ASPRS Annual Conference, March, p. 24–28.
  • Blaschke T, Feizizadeh B, Hölbling D. 2014. Object-based image analysis and digital terrain analysis for locating landslides in the Urmia Lake Basin, Iran. IEEE J Sel Top Appl Earth Observations Remote Sensing. 7(12):4806–4817.
  • Breiman L. 2001. Random forests. Machine Learning. 45(1):5–32.
  • Briem GJ, Benediktsson JA, Sveinsson JR. 2002. Multiple classifiers applied to multisource remote sensing data. IEEE Trans Geosci Remote Sensing. 40(10):2291–2299.
  • Camps-Valls G, Gomez-Chova L, Muñoz-Marí J, Vila-Francés J, Calpe-Maravilla J. 2006. Composite kernels for hyperspectral image classification. IEEE Geosci Remote Sensing Lett. 3(1):93–97.
  • Chen G, Hay GJ, Carvalho LM, Wulder MA. 2012. Object-based change detection. Int J Remote Sens . 33(14):4434–4457.
  • Cheng KS, Wei C, Chang SC. 2004. Locating landslides using multi-temporal satellite images. Advances in Space Research, 33(3), 296–301.
  • Cover TM, Hart P. 1967. Nearest neighbor pattern classification. IEEE Trans Inform Theory. 13(1):21–27.
  • Danneels G, Pirard E, Havenith H-B. 2007. Automatic landslide detection from remote sensing images using supervised classification methods. Geoscience and Remote Sensing Symposium. IGARSS 2007. IEEE International, p. 3014–3017.
  • Debeir O. 2001. Segmentation Supervisée d’Images [Ph.D. thesis]. Faculté des Sciences Appliquées, Université Libre de Bruxelles.
  • Del Frate F, Pacifici F, Schiavon G, Solimini C. 2007. Use of neural networks for automatic classification from high-resolution images. IEEE Trans Geosci Remote Sensing. 45(4):800–809.
  • Dou J, Chang KT, Chen S, Yunus AP, Liu JK, Xia H, Zhu Z. 2015. Automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm. Remote Sensing. 7(4):4318–4342.
  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Han Z, Pham BT. 2020. Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides. 17(3):641–658.
  • Feizizadeh B, Blaschke T, Tiede D, Moghaddam MHR. 2017. Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes. Geomorphology. 293:240–254.
  • Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. 2019. Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection. Remote Sens. 11(2):196.
  • Grabowski S, Jóźwik A, Chen C. 2003. Nearest neighbor decision rule for pixel classification in remote sensing. Frontiers of Remote Sensing Information Processing, p. 315–327. World Scientific, Singapore.
  • Ham J, Chen Y, Crawford MM, Ghosh J. 2005. Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans Geosci Remote Sens. 43:492–501.
  • Herrera Herrera M. 2019. Landslide Detection using Random Forest Classifier [MSc thesis]. Delft University of Technology.
  • Hölbling D, Friedl B, Eisank C. 2015. An object-based approach for semi-automated landslide change detection and attribution of changes to landslide classes in northern Taiwan. Earth Sci Inform. 8(2):327–335.
  • Inglada J. 2007. Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS J Photogramm Remote Sens . 62(3):236–248.
  • Jensen RR, Hardin PJ, Yu G. 2009. Artificial neural networks and remote sensing. Geography Compass. 3(2):630–646.
  • Keyport RN, Oommen T, Martha TR, Sajinkumar KS, Gierke JS. 2018. A comparative analysis of pixel-and object-based detection of landslides from very high-resolution images. Int J Appl Earth Obs Geoinf. 64:1–11.
  • Khorram S, Van Der Wiele CF, Koch FH, Nelson SA, Potts MD. 2016. Principles of applied remote sensing, Springer, New York,USA.
  • Kirschbaum DB, Adler R, Hong Y, Hill S, Lerner-Lam A. 2010. A global landslide catalog for hazard applications: method, results, and limitations. Nat Hazards. 52(3):561–575.
  • Lemmens M. 2011. Geo-information: technologies, applications and the environment. Vol. 5. Springer Science & Business Media, The Netherlands.
  • Li Z, Shi W, Myint SW, Lu P, Wang Q. 2016. Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method. Remote Sens. Environ. 175:215–230.
  • Lu D, Mausel P, Brondizio E, Moran E. 2004. Change detection techniques. Int J Remote Sens. 25(12):2365–2401.
  • Lu P, Stumpf A, Kerle N, Casagli N. 2011. Object-oriented change detection for landslide rapid mapping. IEEE Geosci Remote Sensing Lett. 8(4):701–705.
  • Ma Z, Mei G, Piccialli F. 2020. Machine learning for landslides prevention: a survey. Neural Comput Appl. :1–27. doi: https://link.springer.com/article/10.1007%2Fs00521-020-05529-8
  • MacQueen J. 1967. Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, No. 14, pp. 281–297.
  • Maggiori E, Tarabalka Y, Charpiat G, Alliez P. 2017. Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sensing. 55(2):645–657.
  • Martha TR, Kerle N, Jetten V, van Westen CJ, Kumar KV. 2010. Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods. Geomorphology. 116(1-2):24–36.
  • Martha TR, Kerle N, van Westen CJ, Jetten V, Kumar KV. 2011. Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis. IEEE Trans Geosci Remote Sensing. 49(12):4928–4943.
  • Martha TR, Kerle N, van Westen CJ, Jetten V, Kumar KV. 2012. Object-oriented analysis of multi-temporal panchromatic images for creation of historical landslide inventories. ISPRS J Photogramm Remote Sens. 67:105–119.
  • Melgani F, Bruzzone L. 2002. Support vector machines for classification of hyperspectral remote-sensing images. IEEE International Geoscience and Remote Sensing Symposium, Vol. 1, p. 506–508.
  • Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, … Abderrahmane B. 2020. Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth Sci Rev. 103225. doi: https://www.sciencedirect.com/science/article/abs/pii/S0012825220302713
  • Mountrakis G, Im J, Ogole C. 2011. Support vector machines in remote sensing: A review. ISPRS J Photogramm Remote Sens . 66(3):247–259.
  • O'Connell J, Bradter U, Benton TG. 2015. Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing. ISPRS J Photogramm Remote Sens. 109:165–177.
  • Parker OP. 2013. Object-based segmentation and machine learning classification for landslide detection from multi-temporal WorldView-2 imagery [Ph.D. thesis]. San Francisco State University.
  • Platt RV, Rapoza L. 2008. An evaluation of an object-oriented paradigm for land use/land cover classification. Professional Geographer. 60(1):87–100.
  • Prakash N, Manconi A, Loew S. 2020. Mapping landslides on EO data: Performance of deep learning models vs. traditional machine learning models. Remote Sens. 12(3):346.
  • Puissant A, Rougier S, Stumpf A. 2014. Object-oriented mapping of urban trees using Random Forest classifiers. Int J Appl Earth Obs Geoinf. 26:235–245.
  • Rajan S, Ghosh J, Crawford MM. 2008. An active learning approach to hyperspectral data classification. IEEE Trans Geosci Remote Sensing. 46(4):1231–1242.
  • Scikit-Learn 2020a. GridSearchCV, https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html.
  • Scikit-Learn 2020b. Random Forest, https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html.
  • Shepherd JD, Bunting P, Dymond JR. 2019. Operational large-scale segmentation of imagery based on iterative elimination. Remote Sens. 11(6):658.
  • Steinhaus H. 1956. Sur la division des corp materiels en parties. Bull Acad Polon Sci. 1(804):801.
  • Strahler AH. 1980. The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sens Environ . 10(2):135–163.
  • Stumpf A, Kerle N. 2011. Object-oriented mapping of landslides using Random Forests. Remote Sens Environ . 115(10):2564–2577.
  • Tadono T, Ishida H, Oda F, Naito S, Minakawa K, Iwamoto H. 2014. Precise global DEM generation by ALOS PRISM. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. II-4:71–76.
  • Tavakkoli Piralilou S, Shahabi H, Jarihani B, Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena S, Aryal J. 2019. Landslide detection using multi-scale image segmentation and different machine learning models in the higher Himalayas. Remote Sens. 11(21):2575.
  • Tsangaratos P, Ilia I. 2014. A Supervised Machine Learning Spatial tool for detecting terrain deformation induced by landslide phenomena.
  • Wikipedia. 2020. Elbow method, https://en.wikipedia.org/wiki/Elbow. method (clustering).