Ion SAR data or hyperspectral information. In specific, you’ll find few synergetic wetland classification studies that evaluate the GF-3 and OHS information. One example is, Feng et al. [36] proposed a multibranch convolutional neural network (MBCNN) to fuse Sentinel-1 and Sentinel-2 pictures to map YRD coastal land cover, with an general accuracy of 93.eight along with a Kappa coefficient of 0.93. Zhang et al. [7] mapped the distribution of salt marsh species with all the integration of Sentinel-1 and Sentinel-2 images. On the other hand, only the Sentinel-2 vegetation index and Sentinel-1 backscattering feature are utilized, but the polarization feature of SAR photos isn’t fully utilized. five. Conclusions Wetland classification is actually a difficult activity for remote sensing analysis as a result of similarity of different wetland types in spectrum and texture, but this challenge could possibly be eased by the usage of multi-source satellite information. In this study, a synergetic classification process for GF-3 full-polarization SAR and OHS hyperspectral imagery was proposed as a way to present an updated and dependable spatial distribution map for the whole YRD coastal wetland. 3 classical machine learning algorithms (ML, MD, and SVM) have been employed for the synergetic classification of 18 spectral, index, polarization, and texture attributes. Based on the field investigation and visual interpretation, the overall synergetic classification accuracy of 97 for ML and SVM algorithms is greater than that of single GF-3 or OHS classification, which proves the performance of your fusion of fully polarized SAR information and hyperspectral data in wetland mapping. The spatial distribution of coastal wetlands impacts their ecological functions. Detailed and dependable wetland classification can offer crucial wetland type facts to much better fully grasp the habitat selection of species, migration corridors, as well as the consequences of habitat alter brought on by all-natural and anthropogenic disturbances. The synergy of PolSAR and hyperspectral imagery enables high-resolution classification of wetlands by capturing images all through the year, irrespective of cloud cover. As a result, the proposed approach has the potential to supply Icosabutate medchemexpress correct benefits in different regions.Remote Sens. 2021, 13,21 ofAuthor Contributions: Conceptualization, P.L. and Z.L.; methodology, C.T., P.L., D.L., and Z.L.; formal analysis and validation, C.T., D.L., and P.L.; investigation, C.T., P.L., D.L., Q.Z., M.C., J.L., G.W., and H.W.; sources, P.L., S.Y., and Z.L.; Guretolimod custom synthesis writing–original draft preparation, C.T. and P.L.; writing–review and editing, C.T., P.L., Z.L., H.W., M.C., and Q.Z.; project administration, P.L., Z.L., and H.W.; data curation, C.T., S.Y., and P. L.; visualization, C.T. and P. L.; supervision, P.L., Z.L., and H.W.; funding acquisition, P.L., Z.L., and H.W. All authors have read and agreed to the published version on the manuscript. Funding: This function was jointly supported by the All-natural Science Foundation of China (no. 42041005-4; no. 41806108), National Key Analysis and Improvement System of China (no. 2017YFE0133500; no. 2016YFA0600903), Open Study Fund of State Essential Laboratory of Estuarine and Coastal Investigation (no. SKLEC-KF202002) from East China Standard University, as well as State Key Laboratory of Geodesy and Earth’s Dynamics from Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences (SKLGED2021-5-2). Z.H. Li was supported by the European Space Agency by way of the ESA-MOST DRAGON-5 Project (ref.: 59339).
