ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary by Liying Zhu, Yabin Hu, Guangbo Ren, Na Qiao, Ziyue Meng, Jianbu Wang, Yajie Zhao, Shibao Li, Yi Ma

“…Utilizing GF remote sensing images covering the <italic>tamarix chinensis</italic> research area in the Yellow River Delta, along with field survey data, the model achieves precise classification of different mixed <italic>tamarix chinensis</italic> types. Key results include: 1) The proposed model, trained with only 5&#x0025; of the source domain samples, achieves an overall classification accuracy of 96.52&#x0025; on the target domain samples, which is a 17.61&#x0025; improvement compared with the traditional network U-Net without domain adaptation. 2) Compared with domain adaptation algorithms DAN and S-DMM, the proposed ER-GMMD model demonstrates higher accuracy on the constructed dataset, indicating its potential for high-precision classification of mixed vegetation in coastal wetlands.…”
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