ER-GMMD: Cross-Scene Remote Sensing Classification Method of <italic>Tamarix chinensis</italic> in the Yellow River Estuary
<italic>Tamarix chinensis</italic> effectively prevents coastal erosion, stabilizes the surface of coastal wetlands, and improves the soil quality of saline-alkali land, playing a crucial role in coastal wetland ecosystem restoration. <italic>Tamarix chinensis</italic> exhibi...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10829769/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | <italic>Tamarix chinensis</italic> effectively prevents coastal erosion, stabilizes the surface of coastal wetlands, and improves the soil quality of saline-alkali land, playing a crucial role in coastal wetland ecosystem restoration. <italic>Tamarix chinensis</italic> exhibits a wide distribution that is difficult to capture within a single remote sensing image, while its frequent interspersion with other vegetation results in significant intermixing. The characteristics of mixed <italic>tamarix chinensis</italic> vary substantially across remote sensing images from different scenarios, and spectral confusion further complicates the process. These factors hinder the extraction and alignment of mixed <italic>tamarix chinensis</italic> features during classification, resulting in low cross-scene classification accuracy. To address these challenges, this study proposes a deep learning-based cross-domain classification model, ER-GMMD, which leverages features extracted by deep residual networks for different mixed-growth patterns of <italic>tamarix chinensis,</italic> and integrates dual feature alignment to address the cross-scene classification challenges of mixed-species <italic>tamarix chinensis</italic>. 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% of the source domain samples, achieves an overall classification accuracy of 96.52% on the target domain samples, which is a 17.61% 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. |
|---|---|
| ISSN: | 1939-1404 2151-1535 |