首页|High-Resolution Remote Sensing Farmland Extraction Network Based on Dense-Feature Overlay Fusion and Information Homogeneity Enhancement
High-Resolution Remote Sensing Farmland Extraction Network Based on Dense-Feature Overlay Fusion and Information Homogeneity Enhancement
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NETL
NSTL
IEEE
Deep learning-based high-resolution remote sensing for farmland extraction is a crucial method for obtaining large-scale farmland information. However, variations in crop types, growth conditions, and factors such as narrow edges in farmland lead to lower extraction accuracy and inaccurate boundaries in high-resolution remote sensing. Therefore, this letter proposes a multibranch convolutional neural network (FFENet) that employs a dense-feature overlay fusion module (FFM) and an information homogeneity enhancement module. This network facilitates rapid extraction and dense fusion of information at various scales through the implementation of the dense FM, thereby enhancing the model’s representation of global consistency and local features. The information homogeneity enhancement module further strengthens the information exchange between the bottom and top layers, improves the fusion of feature information across branches, and ensures consistent representation of internal farmland features while enhancing differentiation at the edges. The experimental results demonstrate that the proposed method effectively considers both internal global consistency and local variations in edge information, thereby ensuring the integrity of farmland plots and the continuity of the farmland edges. The quantitative evaluation of the dataset shows that the model performs well in farmland extraction, with overall accuracy (OA) and intersection over union (IoU) reaching 95.41% and 93.74% on the GF-2 dataset and 94.75% and 88.28% on the JL-1 dataset.
NETL
NSTL
IEEE
