Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study

Abstract

The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface areas tend to decrease. It is necessary for obtain baseline datasets such as surface areas and boundaries of water bodies with high accuracy, effectively, economically, and practically by using satellite images in terms of management and planning of lakes. Extracting surface areas of water bodies using image classification algorithms and high-resolution RGB satellite images and evaluating the effectiveness of different image classification algorithms have become an important research domain. In this experimental study, eight different machine learning-based classification approaches, namely, k-nearest neighborhood (kNN), subspaced kNN, support vector machines (SVMs), random forest (RF), bagged tree (BT), Naive Bayes (NB), and linear discriminant (LD), have been utilized to extract the surface areas of lakes. Lastly, autoencoder (AE) classification algorithm was applied, and the effectiveness of all those algorithms was compared. Experimental studies were carried out on three different lakes (Hazar Lake, Salda Lake, Manyas Lake) using high-resolution Turkish RASAT RGB satellite images. The results indicated that AE algorithm obtained the highest accuracy values in both quantitative and qualitative analyses. Another important aspect of this study is that Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI) metrics that can evaluate close to human perception are used for comparison. With this application, it has been shown that overall accuracy calculated from test data may be inadequate in some cases by using SSIM, UIQI, mean squared error (MSE), peak signal to noise ratio (PSNR), and Cohen’s KAPPA metrics. In the last application, the robustness of AE was examined with boxplots.

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References

  1. Acharya TD, Subedi A, Lee DH (2019) Evaluation of machine learning algorithms for surface water extraction in a Landsat 8 scene of nepal. Sensors (Basel) 19:2769. https://doi.org/10.3390/s19122769

    Article  Google Scholar 

  2. Altınsaçlı S, Griffiths HI (2001) Ostracoda (Crustacea) from the Turkish Ramsar site of Lake Kuş (Manyas Gölü). Aquat Conserv 11:217–225. https://doi.org/10.1002/aqc.444

    Article  Google Scholar 

  3. Asomani-Boateng R (2019) Urban wetland planning and management in Ghana: a disappointing implementation. Wetlands 39:251–261. https://doi.org/10.1007/s13157-018-1105-7

    Article  Google Scholar 

  4. Atasever UH (2017) A new unsupervised change detection approach with hybrid clustering for detecting the areal damage after natural disaster. Fresenius Environ Bull 26:3891–3896

    CAS  Google Scholar 

  5. Atasever UH (2019) A novel unsupervised change detection approach based on reconstruction independent component analysis and ABC-Kmeans clustering for environmental monitoring. Environ Monit Assess 191:447. https://doi.org/10.1007/s10661-019-7591-0

    Article  Google Scholar 

  6. Bandos TV, Bruzzone L, Camps-Valls G (2009) Classification of hyperspectral images with regularized linear discriminant analysis. IEEE Trans Geosci Remote 47:862–873. https://doi.org/10.1109/TGRS.2008.2005729

    Article  Google Scholar 

  7. Braithwaite CJR, Zedef V (1996) Hydromagnesite stromatolites and sediments in an alkaline lake, Salda Golu, Turkey. J Sediment Res 66:991–1002

    CAS  Google Scholar 

  8. Breiman L (2001) Random forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  9. Bwangoy J-RB, Hansen MC, Roy DP, Grandi GD, Justice CO (2010) Wetland mapping in the Congo Basin using optical and radar remotely sensed data and derived topographical indices. Remote Sens Environ 114:73–86. https://doi.org/10.1016/j.rse.2009.08.004

    Article  Google Scholar 

  10. Çelik K (2006) Spatial and seasonal variations in chlorophyll-nutrient relationships in the shallow hypertrophic Lake Manyas, Turkey. Environ Monit Assess 117:261–269. https://doi.org/10.1007/s10661-006-0990-z

    CAS  Article  Google Scholar 

  11. Chen L, Jin Z, Michishita R, Cai J, Yue T, Chen B, Xu B (2014a) Dynamic monitoring of wetland cover changes using time-series remote sensing imagery. Ecol Inform 24:17–26. https://doi.org/10.1016/j.ecoinf.2014.06.007

    Article  Google Scholar 

  12. Chen Y, Lin Z, Zhao X, Wang G, Gu Y (2014b) Deep learning-based classification of hyperspectral data. IEEE J Select Topics Appl Earth Observ Remote Sens 7:2094–2107. https://doi.org/10.1109/JSTARS.2014.2329330

    Article  Google Scholar 

  13. Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Select Topics Appl Earth Observ Remote Sens 8:2381–2392. https://doi.org/10.1109/JSTARS.2015.2388577

    Article  Google Scholar 

  14. Chen B, Chen L, Lu M, Xu B (2017) Wetland mapping by fusing fine spatial and hyperspectral resolution images. Ecol Model 353:95–106. https://doi.org/10.1016/j.ecolmodel.2017.01.004

    Article  Google Scholar 

  15. Chen W et al (2020) Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods. Sci Total Environ 701:134979. https://doi.org/10.1016/j.scitotenv.2019.134979

    CAS  Article  Google Scholar 

  16. Chirici G et al (2016) A meta-analysis and review of the literature on the k-nearest neighbors technique for forestry applications that use remotely sensed data. Remote Sens Environ 176:282–294. https://doi.org/10.1016/j.rse.2016.02.001

    Article  Google Scholar 

  17. Chougrad H, Zouaki H, Alheyane O (2018) Deep convolutional neural networks for breast cancer screening. Comput Methods Prog Biomed 157:19–30. https://doi.org/10.1016/j.cmpb.2018.01.011

    Article  Google Scholar 

  18. Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random Forests for Classification In Ecology. Ecology 88:2783–2792. https://doi.org/10.1890/07-0539.1

    Article  Google Scholar 

  19. Danladi IB, Akçer-Ön S (2018) Solar forcing and climate variability during the past millennium as recorded in a high altitude lake: Lake Salda (SW Anatolia). Quat Int 486:185–198. https://doi.org/10.1016/j.quaint.2017.08.068

    Article  Google Scholar 

  20. Das I, Stein A, Kerle N, Dadhwal VK (2012) Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology 179:116–125. https://doi.org/10.1016/j.geomorph.2012.08.004

    Article  Google Scholar 

  21. Davraz A, Varol S, Sener E, Sener S, Aksever F, Kırkan B, Tokgözlü A (2019) Assessment of water quality and hydrogeochemical processes of Salda alkaline lake (Burdur, Turkey). Environ Monit Assess 191:701. https://doi.org/10.1007/s10661-019-7889-y

    CAS  Article  Google Scholar 

  22. Debanshi S, Pal S (2020) Wetland delineation simulation and prediction in deltaic landscape. Ecol Indic 108:105757. https://doi.org/10.1016/j.ecolind.2019.105757

    Article  Google Scholar 

  23. Dereli MA, Tercan E (2020) Assessment of shoreline changes using historical satellite images and geospatial analysis along the Lake Salda in Turkey. Earth Sci Inf 13:709–718. https://doi.org/10.1007/s12145-020-00460-x

    Article  Google Scholar 

  24. Dronova I, Gong P, Clinton NE, Wang L, Fu W, Qi S, Liu Y (2012) Landscape analysis of wetland plant functional types: the effects of image segmentation scale, vegetation classes and classification methods. Remote Sens Environ 127:357–369. https://doi.org/10.1016/j.rse.2012.09.018

    Article  Google Scholar 

  25. Erdogan M, Yilmaz A, Eker O (2016) The georeferencing of RASAT satellite imagery and some practical approaches to increase the georeferencing accuracy. Geocarto Int 31:647–660. https://doi.org/10.1080/10106049.2015.1073367

    Article  Google Scholar 

  26. Eriş KK (2013) Late Pleistocene–Holocene sedimentary records of climate and lake-level changes in Lake Hazar, eastern Anatolia, Turkey. Quat Int 302:123–134. https://doi.org/10.1016/j.quaint.2012.12.024

    Article  Google Scholar 

  27. Eriş KK, Arslan TN, Sabuncu A (2018a) Influences of climate and tectonic on the Middle to Late Holocene Deltaic sedimentation in Lake Hazar, Eastern Turkey. Arab J Sci Eng 43:3685–3697. https://doi.org/10.1007/s13369-017-3021-1

    CAS  Article  Google Scholar 

  28. Eriş KK, Ön SA, Çağatay MN, Ülgen UB, Ön ZB, Gürocak Z, Nagihan Arslan T, Akkoca DB, Damcı E, İnceöz M, Okan ÖÖ (2018b) Late Pleistocene to Holocene paleoenvironmental evolution of Lake Hazar, Eastern Anatolia, Turkey. Quat Int 486:4–16. https://doi.org/10.1016/j.quaint.2017.09.027

    Article  Google Scholar 

  29. Feyisa GL, Meilby H, Fensholt R, Proud SR (2014) Automated water extraction index: a new technique for surface water mapping using Landsat imagery. Remote Sens Environ 140:23–35. https://doi.org/10.1016/j.rse.2013.08.029

    Article  Google Scholar 

  30. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugenics 7:179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x

    Article  Google Scholar 

  31. Foody GM, Mathur A (2004) Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification. Remote Sens Environ 93:107–117. https://doi.org/10.1016/j.rse.2004.06.017

    Article  Google Scholar 

  32. Ge G, Shi Z, Zhu Y, Yang X, Hao Y (2020) Land use/cover classification in an arid desert-oasis mosaic landscape of China using remote sensed imagery: performance assessment of four machine learning algorithms. Global Ecol Conserv 22:e00971. https://doi.org/10.1016/j.gecco.2020.e00971

    Article  Google Scholar 

  33. Gebler D, Kolada A, Pasztaleniec A, Szoszkiewicz K (2020) Modelling of ecological status of Polish lakes using deep learning techniques. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-10731-1

  34. Ghimire B, Rogan J, Galiano VR, Panday P, Neeti N (2012) An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA. GI Sci Remote Sens 49:623–643. https://doi.org/10.2747/1548-1603.49.5.623

    Article  Google Scholar 

  35. Gunen MA, Atasever UH, Besdok E (2020) Analyzing the contribution of training algorithms on deep neural networks for hyperspectral image classification. Photogramm Eng Rem Sci 86:581–588. https://doi.org/10.14358/pers.86.9.581

    Article  Google Scholar 

  36. Gürlük S, Rehber E (2008) A travel cost study to estimate recreational value for a bird refuge at Lake Manyas, Turkey. J Environ Manag 88:1350–1360. https://doi.org/10.1016/j.jenvman.2007.07.017

    Article  Google Scholar 

  37. Halabisky M, Moskal LM, Gillespie A, Hannam M (2016) Reconstructing semi-arid wetland surface water dynamics through spectral mixture analysis of a time series of Landsat satellite images (1984–2011). Remote Sens Environ 177:171–183. https://doi.org/10.1016/j.rse.2016.02.040

    Article  Google Scholar 

  38. He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Chai H, Bian H, Ma J, Chen Y, Wang X, Chapi K, Ahmad BB (2019) Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci Total Environ 663:1–15. https://doi.org/10.1016/j.scitotenv.2019.01.329

    CAS  Article  Google Scholar 

  39. Helvaci C, Mordogan H, Çolak M, Gündogan I (2004) Presence and distribution of lithium in borate deposits and some recent lake waters of West-Central Turkey. Int Geol Rev 46:177–190. https://doi.org/10.2747/0020-6814.46.2.177

    Article  Google Scholar 

  40. Ho TK (1998) Nearest neighbors in random subspaces. In: Advances in Pattern Recognition. Springer, Berlin, pp 640–648

    Google Scholar 

  41. Hore A, Ziou D Image Quality Metrics: PSNR vs. SSIM. In: 2010 20th International Conference on Pattern Recognition, 23-26 Aug. 2010. 2010. pp 2366-2369. https://doi.org/10.1109/ICPR.2010.579

  42. Huang KS, Li ST, Kang XD, Fang LY (2015a) Spectral-spatial hyperspectral image classification based on KNN. Sens Imag 17. https://doi.org/10.1007/s11220-015-0126-z

  43. Huang X, Xie C, Fang X, Zhang L (2015b) Combining pixel- and object-based machine learning for identification of water-body types from urban high-resolution remote-sensing imagery. IEEE J Select Topics Appl Earth Observ Remote Sens 8:2097–2110. https://doi.org/10.1109/JSTARS.2015.2420713

    Article  Google Scholar 

  44. Huang B, Zhao B, Song Y (2018) Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens Environ 214:73–86. https://doi.org/10.1016/j.rse.2018.04.050

    Article  Google Scholar 

  45. Jonathan Cheung-Wai C, Chengquan H, DeFries R (2001) Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting. IEEE Trans Geosci Remote 39:693–695. https://doi.org/10.1109/36.911126

    Article  Google Scholar 

  46. Kaiser J, On B, Arz H, Akcer-On S (2016) Sedimentary lipid biomarkers in the magnesium rich and highly alkaline Lake Salda (south-western Anatolia). J Limnol 75:581–596. https://doi.org/10.4081/jlimnol.2016.1337

    Article  Google Scholar 

  47. Kaplan G, Avdan U (2019) Evaluating the utilization of the red edge and radar bands from sentinel sensors for wetland classification. CATENA 178:109–119. https://doi.org/10.1016/j.catena.2019.03.011

    Article  Google Scholar 

  48. Karafistan A, Arik-Colakoglu F (2005) Physical, chemical and microbiological water quality of the Manyas LakeTurkey. Mitig Adapt Strateg Glob Chang 10:127–143. https://doi.org/10.1007/s11027-005-7835-x

    Article  Google Scholar 

  49. Kazanci N, Girgin S, Dügel M (2004) On the limnology of Salda Lake, a large and deep soda lake in southwestern Turkey: future management proposals. Aquat Conserv Mar Freshwat Ecosyst 14:151–162. https://doi.org/10.1002/aqc.609

    Article  Google Scholar 

  50. Kazancı N, Leroy S, Ileri Ö, Emre Ö, Kibar M, Öncel S (2004) Late Holocene erosion in NW Anatolia from sediments of Lake Manyas, Lake Ulubat and the southern shelf of the Marmara Sea, Turkey. CATENA 57:277–308. https://doi.org/10.1016/j.catena.2003.11.004

    Article  Google Scholar 

  51. Khan A, Sohail A, Zahoora U, Qureshi AS (2020) A survey of the recent architectures of deep convolutional neural networks. Artif Intell Rev 53:5455–5516. https://doi.org/10.1007/s10462-020-09825-6

    Article  Google Scholar 

  52. Külahcı F (2016) Spatiotemporal (four-dimensional) modeling and simulation of uranium (238) in Hazar Lake (Turkey) water. Environ Earth Sci 75:452. https://doi.org/10.1007/s12665-016-5302-5

    CAS  Article  Google Scholar 

  53. Kutluk H (2019) Palynomorphs from Late Holocene sediments of the eutrophic Lake Manyas, NW Anatolia. Rev Palaeobot Palynol 269:1–32. https://doi.org/10.1016/j.revpalbo.2019.06.001

    Article  Google Scholar 

  54. Leroy S, Kazancı N, İleri Ö, Kibar M, Emre O, McGee E, Griffiths HI (2002) Abrupt environmental changes within a late Holocene lacustrine sequence south of the Marmara Sea (Lake Manyas, N-W Turkey): possible links with seismic events. Mar Geol 190:531–552. https://doi.org/10.1016/S0025-3227(02)00361-4

    Article  Google Scholar 

  55. Li W, Fu H, Yu L, Gong P, Feng D, Li C, Clinton N (2016a) Stacked autoencoder-based deep learning for remote-sensing image classification: a case study of African land-cover mapping. Int J Remote Sens 37:5632–5646. https://doi.org/10.1080/01431161.2016.1246775

    Article  Google Scholar 

  56. Li X, Peng L, Hu Y, Shao J, Chi T (2016b) Deep learning architecture for air quality predictions. Environ Sci Pollut Res 23:22408–22417. https://doi.org/10.1007/s11356-016-7812-9

    Article  Google Scholar 

  57. Li Y, Zhang H, Xue X, Jiang Y, Shen Q (2018) Deep learning for remote sensing image classification: a survey. WIREs Data Min Knowledge Discov 8:e1264. https://doi.org/10.1002/widm.1264

    Article  Google Scholar 

  58. Li S, Song W, Fang L, Chen Y, Ghamisi P, Benediktsson JA (2019) Deep learning for hyperspectral image classification: an overview. IEEE Trans Geosci Remote Sens 57:6690–6709. https://doi.org/10.1109/TGRS.2019.2907932

    Article  Google Scholar 

  59. Liu T, Abd-Elrahman A (2018) Deep convolutional neural network training enrichment using multi-view object-based analysis of unmanned aerial systems imagery for wetlands classification. ISPRS J Photogramm 139:154–170. https://doi.org/10.1016/j.isprsjprs.2018.03.006

    Article  Google Scholar 

  60. Liu Y, Wu L (2016) Geological disaster recognition on optical remote sensing images using deep learning. Procedia Comput Sci 91:566–575. https://doi.org/10.1016/j.procs.2016.07.144

    Article  Google Scholar 

  61. Liu P, Choo K-KR, Wang L, Huang F (2017) SVM or deep learning? A comparative study on remote sensing image classification. Soft Comput 21:7053–7065. https://doi.org/10.1007/s00500-016-2247-2

    Article  Google Scholar 

  62. Liu Q, Basu S, Ganguly S, Mukhopadhyay S, DiBiano R, Karki M, Nemani R (2020) DeepSat V2: feature augmented convolutional neural nets for satellite image classification. Remote Sens Lett 11:156–165. https://doi.org/10.1080/2150704X.2019.1693071

    Article  Google Scholar 

  63. Ma L, Liu Y, Zhang X, Ye Y, Yin G, Johnson BA (2019) Deep learning in remote sensing applications: a meta-analysis and review. ISPRS J Photogramm 152:166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015

    Article  Google Scholar 

  64. Makinde EO, Oyelade EO (2020) Land cover mapping using Sentinel-1 SAR and Landsat 8 imageries of Lagos State for 2017. Environ Sci Pollut Res 27:66–74. https://doi.org/10.1007/s11356-019-05589-x

    Article  Google Scholar 

  65. McRoberts RE, Magnussen S, Tomppo EO, Chirici G (2011) Parametric, bootstrap, and jackknife variance estimators for the k-nearest neighbors technique with illustrations using forest inventory and satellite image data. Remote Sens Environ 115:3165–3174. https://doi.org/10.1016/j.rse.2011.07.002

    Article  Google Scholar 

  66. Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote 42:1778–1790. https://doi.org/10.1109/TGRS.2004.831865

    Article  Google Scholar 

  67. MohanRajan SN, Loganathan A, Manoharan P (2020) Survey on land use/land cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges. Environ Sci Pollut Res 27:29900–29926. https://doi.org/10.1007/s11356-020-09091-7

    Article  Google Scholar 

  68. Mohsen A, Elshemy M, Zeidan BA (2018) Change detection for Lake Burullus, Egypt using remote sensing and GIS approaches. Environ Sci Pollut Res 25:30763–30771. https://doi.org/10.1007/s11356-016-8167-y

    CAS  Article  Google Scholar 

  69. Nevavuori P, Narra N, Lipping T (2019) Crop yield prediction with deep convolutional neural networks. Comput Electron Agric 163:104859. https://doi.org/10.1016/j.compag.2019.104859

    Article  Google Scholar 

  70. Okan ÖÖ, Güven A (2019) Hydrochemistry of groundwaters from alluvial and fractured igneous aquifers at the western region of Lake Hazar (Elazığ, Turkey). Arab J Geosci 12(52). https://doi.org/10.1007/s12517-018-4209-8

  71. Ozigis MS, Kaduk JD, Jarvis CH (2019) Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: a case site within the Niger Delta region of Nigeria. Environ Sci Pollut Res 26:3621–3635. https://doi.org/10.1007/s11356-018-3824-y

    Article  Google Scholar 

  72. Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26:217–222. https://doi.org/10.1080/01431160412331269698

    Article  Google Scholar 

  73. Rapinel S, Fabre E, Dufour S, Arvor D, Mony C, Hubert-Moy L (2019) Mapping potential, existing and efficient wetlands using free remote sensing data. J Environ Manag 247:829–839. https://doi.org/10.1016/j.jenvman.2019.06.098

    CAS  Article  Google Scholar 

  74. Rokni K, Ahmad A, Solaimani K, Hazini S (2015) A new approach for surface water change detection: Integration of pixel level image fusion and image classification techniques. Int J Appl Earth Obs Geoinf 34:226–234. https://doi.org/10.1016/j.jag.2014.08.014

    Article  Google Scholar 

  75. Russell MJ, Ingham JK, Zedef V, Maktav D, Sunar F, Hall AJ, Fallick AE (1999) Search for signs of ancient life on Mars: expectations from hydromagnesite microbialites, Salda Lake, Turkey. J Geol Soc 156:869–888. https://doi.org/10.1144/gsjgs.156.5.0869

    CAS  Article  Google Scholar 

  76. Satir O (2016) Comparing the satellite image transformation techniques for detecting and monitoring the continuous snow cover and glacier in Cilo mountain chain Turkey. Ecol Indic 69:261–268. https://doi.org/10.1016/j.ecolind.2016.04.032

    Article  Google Scholar 

  77. Sharma A, Liu X, Yang X, Shi D (2017) A patch-based convolutional neural network for remote sensing image classification. Neural Netw 95:19–28. https://doi.org/10.1016/j.neunet.2017.07.017

    Article  Google Scholar 

  78. Shen DG, Wu GR, Suk HI (2017) Deep learning in medical image analysis. In: Yarmush ML (ed) Annual Review of Biomedical Engineering, Annual Review of Biomedical Engineering, vol 19, pp 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442

    Google Scholar 

  79. Shen G, Yang X, Jin Y, Xu B, Zhou Q (2019) Remote sensing and evaluation of the wetland ecological degradation process of the Zoige Plateau Wetland in China. Ecol Indic 104:48–58. https://doi.org/10.1016/j.ecolind.2019.04.063

    Article  Google Scholar 

  80. Timm T, Arslan N, Ruzgar M, Martinsson S, Erseus C (2013) Oligochaeta (Annelida) of the profundal of Lake Hazar (Turkey), with description of Potamothrix alatus hazaricus n. ssp. Zootaxa 3716:144–156

    Article  Google Scholar 

  81. Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag

  82. Wang X, Zhang Q, Zhang S (2018) Azimuth selection for sea level measurements using geodetic GPS receivers. Adv Space Res 61:1546–1557. https://doi.org/10.1016/j.asr.2018.01.002

    Article  Google Scholar 

  83. Weiss M, Jacob F, Duveiller G (2020) Remote sensing for agricultural applications: A meta-review. Remote Sens Environ 236:111402. https://doi.org/10.1016/j.rse.2019.111402

    Article  Google Scholar 

  84. West H, Quinn N, Horswell M (2019) Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities. Remote Sens Environ 232:111291. https://doi.org/10.1016/j.rse.2019.111291

    Article  Google Scholar 

  85. Yang Y, Liu Y, Zhou M, Zhang S, Zhan W, Sun C, Duan Y (2015) Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach. Remote Sens Environ 171:14–32. https://doi.org/10.1016/j.rse.2015.10.005

    Article  Google Scholar 

  86. Yang J, Griffiths J, Zammit C (2019) National classification of surface–groundwater interaction using random forest machine learning technique. River Res Appl 35:932–943. https://doi.org/10.1002/rra.3449

    Article  Google Scholar 

  87. Zhai K, Wu X, Qin Y, Du P (2015) Comparison of surface water extraction performances of different classic water indices using OLI and TM imageries in different situations. Geo-Spat Inform Sci 18:32–42. https://doi.org/10.1080/10095020.2015.1017911

    Article  Google Scholar 

  88. Zhang F, Tiyip T, H-t K, Johnson VC, Wang J, Nurmemet I (2016) Improved water extraction using Landsat TM/ETM+ images in Ebinur Lake, Xinjiang, China. Remote Sens Appl 4:109–118. https://doi.org/10.1016/j.rsase.2016.08.001

    Article  Google Scholar 

  89. Zhao W, Chellappa R, Nandhakumar N (1998) Empirical performance analysis of linear discriminant classifiers. In: Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), 25-25 June 1998. pp 164-169. https://doi.org/10.1109/CVPR.1998.698604

  90. Zhao W, Guo Z, Yue J, Zhang X, Luo L (2015) On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery. Int J Remote Sens 36:3368–3379. https://doi.org/10.1080/2150704X.2015.1062157

    Article  Google Scholar 

  91. Zhong Y, Ma A, Ong YS, Zhu Z, Zhang L (2018) Computational intelligence in optical remote sensing image processing. Appl Soft Comput 64:75–93. https://doi.org/10.1016/j.asoc.2017.11.045

    Article  Google Scholar 

  92. Zhu S, Lu H, Ptak M, Dai J, Ji Q (2020) Lake water-level fluctuation forecasting using machine learning models: a systematic review. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-020-10917-7

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The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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ET and UHA designed the research, performed the study, and wrote the paper.

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Correspondence to Emre Tercan.

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Tercan, E., Atasever, U.H. Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study. Environ Sci Pollut Res (2021). https://doi.org/10.1007/s11356-021-12893-y

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Keywords

  • Machine learning
  • Autoencoder
  • Remote sensing
  • Image classification
  • Water body extraction
  • RASAT
  • Environmental monitoring
  • Lake management