[1] [1] MAHDAVI S, SALEHI B, GRANGER J,et al. Remote sensing for wetland classification: A comprehensive review[J]. GIScience & Remote Sensing, 2018, 55(5): 623-658. DOI: 10.1080/15481603.2017.1419602

[2] [2] TINER R W. Wetland indicators: A guide to wetland formation, identification, delineation, classification, and mapping[M].Boca Raton: CRC press, 2016.

[3] [3] MAO D H, WANG Z M, DU B J,et al. National wetland mapping in China: A new product resulting from object-based and hierarchical classification of Landsat 8 OLI images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 164: 11-25. DOI: 10.1016/j.isprsjprs.2020.03.020

[5] [5] MITSCH W J, BERNAL B, HERNANDEZ M E. Ecosystem services of wetlands[J]. International Journal of Biodiversity Science, Ecosystem Services & Management, 2015, 11(1): 1-4. DOI: 10.1080/21513732.2015.1006250

[6] [6] MOHAMMADIMANESH F, SALEHI B, MAHDIANPARI M,et al. Wetland water level monitoring using Interferometric Synthetic Aperture Radar (InSAR): A review[J]. Canadian Journal of Remote Sensing, 2018, 44(4): 247-262. DOI: 10.1080/07038992.2018.1477680

[7] [7] WERNER B A, JOHNSON W C, GUNTENSPERGEN G R. Evidence for 20th century climate warming and wetland drying in the North American Prairie Pothole Region[J]. Ecology and Evolution, 2013, 3(10): 3471-3482. DOI: 10.1002/ece3.731

[8] [8] DAVIDSON N C.Ramsar convention on wetlands: Scope and implementation[M].The Wetland Book. Dordrecht: Springer Netherlands, 2018: 451-458. DOI: 10.1007/978-90-481-9659-3_113

[9] [9] MAHDIANPARI M, SALEHI B, MOHAMMADIMANESH F,et al. The first wetland inventory map of Newfoundland at a spatial resolution of 10 m using Sentinel-1 and Sentinel-2 data on the Google Earth Engine cloud computing platform[J]. Remote Sensing, 2019, 11(1): 43. DOI: 10.3390/rs11010043

[10] [10] FOURNIER R A, GRENIER M, LAVOIE A,et al. Towards a strategy to implement the Canadian wetland Inventory using satellite remote sensing[J]. Canadian Journal of Remote Sensing, 2007, 33(Sup1): S1-S16. DOI: 10.5589/m07-051

[11] [11] LAROCQUE A, PHIRI C, LEBLON B,et al. Wetland mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR data in Southern New Brunswick, Canada[J]. Remote Sensing, 2020, 12(13): 2095. DOI: 10.3390/rs12132095

[12] [12] MOHAMMADIMANESH F, SALEHI B, MAHDIANPARI M,et al. Full and simulated compact polarimetry SAR responses to Canadian wetlands: Separability analysis and classification[J]. Remote Sensing, 2019, 11(5): 516. DOI: 10.3390/rs11050516

[13] [13] AMANI M, BRISCO B, MAHDAVI S,et al. Evaluation of the Landsat-based Canadian wetland inventory map using multiple sources: Challenges of large-scale wetland classification using remote sensing[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 14: 32-52.

[14] [14] MCCARTHY M J, RADABAUGH K R, MOYER R P,et al. Enabling efficient, large-scale high-spatial resolution wetland mapping using satellites[J]. Remote Sensing of Environment, 2018, 208: 189-201. DOI: 10.1016/j.rse.2018.02.021

[15] [15] ADAM E, MUTANGA O, RUGEGE D. Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review[J]. Wetlands Ecology and Management, 2010, 18(3): 281-296. DOI: 10.1007/s11273-009-9169-z

[16] [16] MCCARTHY M J, MERTON E J, MULLER-KARGER F E. Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 40: 11-18. DOI: 10.1016/j.jag.2015.03.011

[17] [17] LANE C R, ANENKHONOV O, LIU H X,et al. Classification and inventory of freshwater wetlands and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using high-resolution satellite imagery[J]. Wetlands Ecology and Management, 2015, 23(2): 195-214. DOI: 10.1007/s11273-014-9369-z

[18] [18] ZIKOPOULOS P, EATON C. Understanding big data: Analytics for enterprise class Hadoop and streaming data[M].New York: McGraw-Hill Osborne Media, 2011.

[23] [23] GOODFELLOW I, BENGIO Y, COURVILLE A. Deep learning[M]. Cambridge, MA: MIT Press, 2016.

[24] [24] VALI A, COMAI S, MATTEUCCI M. Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review[J]. Remote Sensing, 2020, 12(15): 2495. DOI: 10.3390/rs12152495

[25] [25] ROGAN J, CHEN D M. Remote sensing technology for mapping and monitoring land-cover and land-use change[J]. Progress in Planning, 2004, 61(4): 301-325. DOI: 10.1016/S0305-9006(03)00066-7

[26] [26] GONZALEZ E, GONZLEZ TRILLA G, SAN MARTIN L,et al. Vegetation patterns in a South American coastal wetland using high-resolution imagery[J]. Journal of Maps, 2019, 15(2): 642-650. DOI: 10.1080/17445647.2019.1644545

[27] [27] DECHKA J A, FRANKLIN S E, WATMOUGH M D,et al. Classification of wetland habitat and vegetation communities using multi-temporal Ikonos imagery in southern Saskatchewan[J]. Canadian Journal of Remote Sensing, 2002, 28(5): 679-685. DOI: 10.5589/m02-064

[28] [28] ADAM E, MURERIWA N, NEWETE S. Mapping Prosopis Glandulosa (mesquite) in the semi-arid environment of South Africa using high-resolution WorldView-2 imagery and machine learning classifiers[J]. Journal of Arid Environments, 2017, 145: 43-51. DOI: 10.1016/j.jaridenv.2017.05.001

[29] [29] MCCARTHY M J, MERTON E J, MULLER-KARGER F E. Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 40: 11-18. DOI: 10.1016/j.jag.2015.03.011

[30] [30] AMANI M, SALEHI B, MAHDAVI S,et al. Wetland classification using multi-source and multi-temporal optical remote sensing data in Newfoundland and Labrador, Canada[J]. Canadian Journal of Remote Sensing, 2017, 43(4): 360-373. DOI: 10.1080/07038992.2017.1346468

[31] [31] MITSCH W J, GOSSELINK J G. Wetlands[M]. Hoboken, NJ: John Wiley & Sons, 2015.

[32] [32] SOUTH R. Biogeography and Ecology of the Island of Newfoundland[M]. Cham: Springer Netherlands, 1983.

[33] [33] HE Z, HE D, MEI X Q,et al. Wetland classification based on a new efficient generative adversarial network and Jilin-1 satellite image[J].Remote Sensing, 2019, 11(20): 2455. DOI: 10.3390/rs11202455

[34] [34] JAMALI A, MAHDIANPARI M, MOHAMMADIMANESH F,et al. A synergic use of Sentinel-1 and Sentinel-2 imagery for complex wetland classification using Generative Adversarial Network (GAN) scheme[J]. Water, 2021, 13(24): 3601. DOI: 10.3390/w13243601

[35] [35] LIU M, FU B L, XIE S Y,et al. Comparison of multisource satellite images for classifying marsh vegetation using DeepLabV3 Plus deep learning algorithm[J]. Ecological Indicators, 2021, 125: 107562. DOI: 10.1016/j.ecolind. 2021.107562

[36] [36] SUN Z Y, JIANG W G, LING Z Y,et al. Using multisource high-resolution remote sensing data (2 m) with a habitat-tide-semantic segmentation approach for mangrove mapping[J].Remote Sensing, 2023, 15(22): 5271. DOI: 10.3390/rs15225271

[37] [37] ZHANG Y, WANG X, CAI J Y,et al. MW-SAM: Mangrove wetland remote sensing image segmentation network based on segment anything model[J]. IET Image Processing, 2024, 18(14): 4503-4513. DOI: 10.1049/ipr2.13263

[38] [38] CHEN Z Z, CHEN J J, YUE Y M,et al. Tradeoffs among multi-source remote sensing images, spatial resolution, and accuracy for the classification of wetland plant species and surface objects based on the MRS_DeepLabV3+ model[J].Ecological Informatics, 2024, 81: 102594. DOI: 10.1016/j.ecoinf.-2024.102594

[39] [39] EVERITT J H, YANG C, FLETCHER R S,et al. Using aerial color-infrared photography and QuickBird satellite imagery for mapping wetland vegetation[J]. Geocarto International, 2004, 19(4): 15-22. DOI: 10.1080/10106040408542323

[40] [40] DU L, MCCARTY G W, ZHANG X,et al. Mapping forested wetland inundation in the Delmarva Peninsula, USA using deep convolutional neural networks[J]. Remote Sensing, 2020, 12(4): 644. DOI: 10.3390/rs12040644

[41] [41] REZAEE M, MAHDIANPARI M, ZHANG Y,et al. Deep convolutional neural network for complex wetland classification using optical remote sensing imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(9): 3030-3039. DOI: 10.1109/JSTARS.2018.2846178

[42] [42] SONG Y M, ZHANG J. Monitoring and simulating the distribution of phytoplankton in constructed wetlands based on SPOT 6 images[J]. Open Geosciences, 2021, 13(1): 454-468. DOI: 10.1515/geo-2020-0243

[43] [43] DARLING E M, RAUDSEPS J G. Non-parametric unsupervised learning with applications to image classification[J]. Pattern Recognition, 1970, 2(4): 313-335. DOI: 10.1016/0031-3203(70)90021-X

[44] [44] BLANZIERI E, MELGANI F. Nearest neighbor classification of remote sensing images with the maximal margin principle[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6): 1804-1811. DOI: 10.1109/TGRS. 2008.916090

[45] [45] GONALVES M L, NETTO M L A, COSTA J A F,et al. An unsupervised method of classifying remotely sensed images using Kohonen self-organizing maps and agglomerative hierarchical clustering methods[J]. International Journal of Remote Sensing, 2008, 29(11): 3171-3207. DOI: 10.1080/01431160701442146

[46] [46] DRONOVA I, GONG P, WANG L. Object-based analysis and change detection of major wetland cover types and their classification uncertainty during the low water period at Poyang Lake, China[J]. Remote Sensing of Environment, 2011, 115(12): 3220-3236. DOI: 10.1016/j.rse.2011.07.006

[47] [47] BERHANE T M, LANE C R, WU Q S,et al. Decisiontree, rule-based, and random forest classification of high-resolution multispectral imagery for wetland mapping and inventory[J]. Remote Sensing, 2018, 10(4): 580. DOI: 10.3390/rs10040580

[48] [48] SALEHI HIKOUEI I, KIM S S, MISHRA D R. Machinelearning classification of soil bulk density in salt marsh environments[J]. Sensors, 2021, 21(13): 4408. DOI: 10.3390/s21134408

[49] [49] MOAYEDI H, JAMALI A, GIBRIL M B A,et al. Evaluation of tree-base data mining algorithms in land used/land cover mapping in a semi-arid environment through Landsat-8 OLI image；Shiraz, Iran[J]. Geomatics, Natural Hazards and Risk, 2020, 11(1): 724-741. DOI: 10.1080/19475705. 2020.1745902

[50] [50] CARLE M V, WANG L, SASSER C E. Mapping freshwater marsh species distributions using WorldView-2 high-resolution multispectral satellite imagery[J]. International Journal of Remote Sensing, 2014, 35(13): 4698-4716. DOI: 10.1080/01431161.2014.919685

[51] [51] MARTNEZ PRENTICE R, VILLOSLADA PECIA M, WARD R D,et al. Machine learning classification and accuracy assessment from high-resolution images of coastal wetlands[J]. Remote Sensing, 2021, 13(18): 3669. DOI: 10.3390/rs13183669

[52] [52] WANG X X, GAO X W, ZHANG Y Z,et al. Land-cover classification of coastal wetlands using the RF algorithm for Worldview-2 and Landsat 8 images[J]. Remote Sensing, 2019, 11(16): 1927. DOI: 10.3390/rs11161927

[53] [53] HAN X S, PAN J Y, DEVLIN A T. Remote sensing study of wetlands in the Pearl River Delta during 1995~2015 with the support vector machine method[J]. Frontiers of Earth Science, 2018, 12（3）: 521-531. DOI: 10.1007/s11707-017-0672-x

[55] [55] MOUNTRAKIS G, IM J, OGOLE C. Support vector machines in remote sensing: A review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(3): 247-259. DOI: 10.1016/j.isprsjprs.2010.11.001

[56] [56] DEVAL K, JOSHI P K. Vegetation type and land cover mapping in a semi-arid heterogeneous forested wetland of India: Comparing image classification algorithms[J]. Environment, Development and Sustainability, 2022, 24(3): 3947-3966. DOI: 10.1007/s10668-021-01596-6

[57] [57] ZHAO W Z, DU S H. Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4544-4554. DOI: 10.1109/TGRS.2016.2543748

[59] [59] ZHAO W Z, DU S H. Learning multiscale and deep representations for classifying remotely sensed imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 113: 155-165. DOI: 10.1016/j.isprsjprs.2016.01.004

[60] [60] FARABET C, COUPRIE C, NAJMAN L,et al. Learning hierarchical features for scene labeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1915-1929. DOI: 10.1109/TPAMI.2012.231

[61] [61] HE K M, ZHANG X Y, REN S Q,et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE, 2016: 770-778. DOI: 10.1109/CVPR. 2016.90

[62] [62] MARTINS V S, KALEITA A L, GELDER B K,et al. Deep neural network for complex open-water wetland mapping using high-resolution WorldView-3 and Airborne LiDAR data[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 93: 102215. DOI: 10.1016/j.jag.2020.102215

[63] [63] ZHU X X, TUIA D, MOU L C,et al. Deep learning in remote sensing: A comprehensive review and list of resources[J]. IEEE Geoscience and Remote Sensing Magazine, 2017, 5(4): 8-36. DOI: 10.1109/MGRS.2017.2762307

[64] [64] CHENG G, XIE X X, HAN J W,et al. Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 3735-3756.

[65] [65] MAHDIANPARI M, SALEHI B, REZAEE M,et al. Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery[J]. Remote Sensing, 2018, 10(7): 1119. DOI: 10.3390/rs10071119

[66] [66] JAMALI A, MAHDIANPARI M, BRISCO B,et al. Comparing solo versus ensemble convolutional neural networks for wetland classification using multi-spectral satellite imagery[J]. Remote Sensing, 2021, 13(11): 2046. DOI: 10.3390/rs13112046

[67] [67] CHEN M M, KE Y H, BAI J H,et al. Monitoring early stage invasion of exotic Spartina alterniflora using deep-learning super-resolution techniques based on multisource high-resolution satellite imagery: A case study in the Yellow River Delta, China[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 92: 102180. DOI: 10.1016/j.jag.2020.102180

[68] [68] LI C, CUI H W, TIAN X L. A novel CA-RegNet model for Macau wetlands auto segmentation based on GF-2 remote sensing images[J]. Applied Sciences, 2023, 13(22): 12178. DOI: 10.3390/app132212178

[70] [70] SONG T A, CHOWDHURY S R, YANG F,et al. PET image super-resolution using generative adversarial networks[J].Neural Networks, 2020, 125: 83-91. DOI: 10.1016/j.neunet.2020.01.029

[72] [72] HAN W, FENG R Y, WANG L Z,et al. A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 145: 23-43. DOI: 10.1016/j.isprsjprs.2017.11.004

[73] [73] SUH S, LEE H, LUKOWICZ P,et al. CEGAN: Classification enhancement generative adversarial networks for unraveling data imbalance problems[J]. Neural Networks, 2021, 133: 69-86. DOI: 10.1016/j.neunet.2020.10.004

[74] [74] ZHANG H Y, SONG Y Y, HAN C,et al. Remote sensing image spatiotemporal fusion using a generative adversarial network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5): 4273-4286. DOI: 10.1109/TGRS. 2020.3010530