[1] [1] MAHDAVIS， SALEHIB， GRANGERJ， 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] TINERR W. Wetland indicators：A guide to wetland formation，identification， delineation，classification，and mapping［M］.Boca Raton： CRC press， 2016.

[3] [3] MAOD H， WANGZ M， DUB 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

[4] [4] YANGYongxing. Main characteristics， progress and prospect of international wetland science research［J］. Progress in Geography， 2002， 21（2）： 111-120.

[5] [5] MITSCHW J， BERNALB， HERNANDEZM 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] MOHAMMADIMANESHF， SALEHIB， MAHDIANPARIM， 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] WERNERB A， JOHNSONW C， GUNTENSPERGENG 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] DAVIDSONN 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] MAHDIANPARIM， SALEHIB， MOHAMMADIMANESHF， 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] FOURNIERR A，GRENIERM，LAVOIEA，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] LAROCQUEA， PHIRIC， LEBLONB， 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] MOHAMMADIMANESHF， SALEHIB， MAHDIANPARIM， 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] AMANIM， BRISCOB， MAHDAVIS， 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] MCCARTHYM J， RADABAUGHK R， MOYERR 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] ADAME， MUTANGAO， RUGEGED. 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] MCCARTHYM J， MERTONE J， MULLER-KARGERF 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] LANEC R， ANENKHONOVO， LIUH 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] ZIKOPOULOSP， EATONC. Understanding big data： Analytics for enterprise class Hadoop and streaming data［M］. New York： McGraw-Hill Osborne Media， 2011.

[19] [19] YOUPeipei， LIUZhenbo， XIEJiawei， et al. Research on deep learning algorithm remote sensing classification of Jiangsu coastal wetlands based on GF-2 image［J］. Resources and Environment in the Yangtze Basin， 2021， 30（7）： 1659-1669.

[20] [20] TANGYuqi. Research on object-oriented detection of multi-feature changes in cities with high resolution images［D］. Wuhan： Wuhan University， 2013.

[21] [21] ZHUHongchun， CAILijie， LIUHaiying， et al. Optimal segmentation scale calculation for high-resolution remote sensing image［J］. Science of Surveying and Mapping， 2015， 40（3）： 71-75.

[22] [22] TAOYiting. Research on classification method of high spatial resolution remote sensing images based on deep learning［D］. Wuhan： Wuhan University， 2019.

[23] [23] GOODFELLOWI， BENGIOY， COURVILLEA. Deep learning［M］. Cambridge， MA： MIT Press， 2016.

[24] [24] VALIA， COMAIS， MATTEUCCIM. 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] ROGANJ， CHEND 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] GONZALEZE， GONZÁLEZ TRILLAG， MARTIN LSAN， 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] DECHKAJ A， FRANKLINS E， WATMOUGHM 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] ADAME，MURERIWAN， NEWETES. 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] MCCARTHYM J， MERTONE J， MULLER-KARGERF 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] AMANIM， SALEHIB， MAHDAVIS， 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] MITSCHW J， GOSSELINKJ G. Wetlands［M］. Hoboken， NJ： John Wiley & Sons， 2015.

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

[33] [33] HEZ， HED， MEIX 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] JAMALIA， MAHDIANPARIM， MOHAMMADIMANESHF， 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] LIUM， FUB L， XIES Y， et al. Comparison of multi-source 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] SUNZ Y， JIANGW G， LINGZ 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] ZHANGY， WANGX， CAIJ 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] CHENZ Z， CHENJ J， YUEY 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］.Ecolo-gical Informatics，2024，81：102594. DOI：10.1016/j.ecoinf.-2024.102594

[39] [39] EVERITTJ H， YANGC， FLETCHERR 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] DUL， MCCARTYG W， ZHANGX， 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] REZAEEM， MAHDIANPARIM， ZHANGY， 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] SONGY M， ZHANGJ. 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] DARLINGE M， RAUDSEPSJ 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] BLANZIERIE， MELGANIF. 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] GONÇALVESM L， NETTOM L A， COSTAJ 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] DRONOVAI， GONGP， WANGL. 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] BERHANET M， LANEC R， WUQ S， et al. Decision-tree， 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 HIKOUEII， KIMS S， MISHRAD R. Machine-learning classification of soil bulk density in salt marsh environments［J］. Sensors， 2021， 21（13）： 4408. DOI： 10.3390/s21134408

[49] [49] MOAYEDIH， JAMALIA， GIBRILM 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] CARLEM V， WANGL， SASSERC 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] MARTÍNEZ PRENTICER， VILLOSLADA PECIÑAM， WARDR 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] WANGX X， GAOX W， ZHANGY 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] HANX S，PANJ Y，DEVLINA 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

[54] [54] WANGLiguo， ZHAOLiang， LIUDanfeng. A review on the application of SVM in hyperspectral image processing［J］. Journal of Harbin Engineering University， 2018， 39（6）： 973-983.

[55] [55] MOUNTRAKISG， IM J， OGOLEC. 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] DEVALK， JOSHIP 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] ZHAOW Z， DUS 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

[58] [58] SUNXian， FUKun， WANGHongqi. Spatial semantic objects-based hybrid learning method for automatic complicated scene classification［J］. Journal of Electronics & Information Technology， 2011， 33（2）： 347-354.

[59] [59] ZHAOW Z， DUS 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] FARABETC， COUPRIEC， NAJMANL， 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] HEK M，ZHANGX Y，RENS 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] MARTINSV S， KALEITAA L， GELDERB 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] ZHUX X， TUIAD， MOUL 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] CHENGG， XIEX X， HANJ 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] MAHDIANPARIM，SALEHIB，REZAEEM，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] JAMALIA， MAHDIANPARIM， BRISCOB， 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] CHENM M， KEY H， BAIJ 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] LIC， CUIH W， TIANX 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

[69] [69] CAOYangjie，JIALili，CHENYongxia，et al.Review of computer vision based on generative adversarial networks［J］.Journal of Image and Graphics，2018，23（10）：1433-1449.

[70] [70] SONGT A，CHOWDHURYS R，YANGF， 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

[71] [71] YEChen，GUANWei. A review of application of generative adversarial networks［J］. Journal of Tongji University（Natural Science Edition）， 2020，48（4）：591-601.

[72] [72] HANW， FENGR Y， WANGL 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， LEEH， LUKOWICZP， 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] ZHANGH Y， SONGY Y， HANC， 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