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Article

Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery

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College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, China
2
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
3
School of Geomatics and Prospecting Engineering, Jilin Jianzhu University, Changchun 130118, China
4
Jilin Provincial Meteorological Information and Network Center, Changchun 130062, China
*
Author to whom correspondence should be addressed.
Academic Editor: Miklas Scholz
Water 2022, 14(1), 82; https://doi.org/10.3390/w14010082
Received: 9 December 2021 / Revised: 30 December 2021 / Accepted: 31 December 2021 / Published: 3 January 2022
(This article belongs to the Special Issue Application of Remote Sensing Technology to Water-Related Ecosystems)
The development of advanced and efficient methods for mapping and monitoring wetland regions is essential for wetland resources conservation, management, and sustainable development. Although remote sensing technology has been widely used for detecting wetlands information, it remains a challenge for wetlands classification due to the extremely complex spatial patterns and fuzzy boundaries. This study aims to implement a comprehensive and effective classification scheme for wetland land covers. To achieve this goal, a novel object-based multigrained cascade forest (OGCF) method with multisensor data (including Sentinel-2 and Radarsat-2 remote sensing imagery) was proposed to classify the wetlands and their adjacent land cover classes in the wetland National Natural Reserve. Moreover, a hybrid selection method (ReliefF-RF) was proposed to optimize the feature set in which the spectral and polarimetric decomposition features are contained. We obtained six spectral features from visible and shortwave infrared bands and 10 polarimetric decomposition features from the H/A/Alpha, Pauli, and Krogager decomposition methods. The experimental results showed that the OGCF method with multisource features for land cover classification in wetland regions achieved the overall accuracy and kappa coefficient of 88.20% and 0.86, respectively, which outperformed the support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), and deep neural network (DNN). The accuracy of the wetland classes ranged from 75.00% to 97.53%. The proposed OGCF method exhibits a good application potential for wetland land cover classification. The classification scheme in this study will make a positive contribution to wetland inventory and monitoring and be able to provide technical support for protecting and developing natural resources. View Full-Text
Keywords: object-based multigrained cascade forest; wetland classification; feature selection; Sentinel-2; Radarsat-2 object-based multigrained cascade forest; wetland classification; feature selection; Sentinel-2; Radarsat-2
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MDPI and ACS Style

Liu, H.; Jiang, Q.; Ma, Y.; Yang, Q.; Shi, P.; Zhang, S.; Tan, Y.; Xi, J.; Zhang, Y.; Liu, B.; Gao, X. Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery. Water 2022, 14, 82. https://doi.org/10.3390/w14010082

AMA Style

Liu H, Jiang Q, Ma Y, Yang Q, Shi P, Zhang S, Tan Y, Xi J, Zhang Y, Liu B, Gao X. Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery. Water. 2022; 14(1):82. https://doi.org/10.3390/w14010082

Chicago/Turabian Style

Liu, Huaxin, Qigang Jiang, Yue Ma, Qian Yang, Pengfei Shi, Sen Zhang, Yang Tan, Jing Xi, Yibo Zhang, Bin Liu, and Xin Gao. 2022. "Object-Based Multigrained Cascade Forest Method for Wetland Classification Using Sentinel-2 and Radarsat-2 Imagery" Water 14, no. 1: 82. https://doi.org/10.3390/w14010082

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