Remote Sensing (May 2025)
Multilevel Feature Cross-Fusion-Based High-Resolution Remote Sensing Wetland Landscape Classification and Landscape Pattern Evolution Analysis
Abstract
Analyzing wetland landscape pattern evolution is crucial for managing wetland resources. High-resolution remote sensing serves as a primary method for monitoring wetland landscape patterns. However, the complex landscape types and spatial structures of wetlands pose challenges, including interclass similarity and intraclass spatial heterogeneity, leading to the low separability of landscapes and difficulties in identifying fragmented and small objects. To address these issues, this study proposes the multilevel feature cross-fusion wetland landscape classification network (MFCFNet), which combines the global modeling capability of Swin Transformer with the local detail-capturing ability of convolutional neural networks (CNNs), facilitating discerning intraclass consistency and interclass differences. To alleviate the semantic confusion caused by different-level features with semantic gaps during fusion, we introduce a deep–shallow feature cross-fusion (DSFCF) module between the encoder and the decoder. We incorporate global–local attention block (GLAB) to aggregate global contextual information and local detail. The constructed Shengjin Lake Wetland Gaofen Image Dataset (SLWGID) is utilized to evaluate the performance of MFCFNet, achieving evaluation metric results of the OA, mIoU, and F1 score of 93.23%, 78.12%, and 87.05%, respectively. MFCFNet is used to classify the wetland landscape of Shengjin Lake from 2013 to 2023. A landscape pattern evolution analysis is conducted, focusing on landscape transitions, area changes, and pattern characteristic variations. The method demonstrates effectiveness for the dynamic monitoring of wetland landscape patterns, providing valuable insights for wetland conservation.
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