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Received：27 May 2022，
Published：07 December 2023
移动端阅览
耕地作为国家粮食生产的重要保障，其空间分布是粮食安全评估、土地资源管理等领域的主要依据。为解决现有的耕地信息提取方法忽视地块的差异化特征和边缘细节蕴含的丰富信息，且提取结果碎片化、边界模糊问题。本研究以耕地为研究对象，采用一种结合EfficientNet骨干网络和U型框架构建的改进型耕地信息提取模型BECU-Net（Boundary Enhancement Classification U-Net），并为实现边缘特征和深度特征的信息互补，设计由CoT模块（Contextual Transformer Module）、门控卷积、scSE（Spatial-Channel Sequeeze and Excitation）注意力机制形成的边缘分支子网络，来提高模型处理边界信息的专注度。同时，构建含约束项的联合型边缘增强损失函数BE-LOSS（Boundary Enhancement Loss）进一步完善模型运算性能。使用GID高分二号RGB-NIR四波段数据，与梯度、指数、纹理特征图共同构建耕地特征机制。并分别与不同网络结构、不同损失函数的模型进行对比。结果表明：改进算法的总体精度和F1分数均有改善，相比于DeeplabV3+网络，提取精度提升2.24%，F1分数提升1.77%。本研究提出的新算法可为进一步解决耕地信息提取时边界模糊问题提供技术参考，为复杂交界的精准划分提供理论支撑。
Cultivated land cover
as an important technical index to reflect the dynamic changes of human activities and the utilization degree of land resources
has been widely utilized in the fields of food security assessment and land management decision making. Existing information extraction methods ignore the differential characteristics of the plots and the rich information found in edge details
which results in fragmented extraction results with fuzzy boundaries. Therefore
an improved model that couples semantic segmentation model and edge enhancement is proposed to better solve the problem of insufficient fitting of cultivated land edges and fully utilize the rich semantic features and edge information in remote sensing images. The edge loss is designed accordingly to further improve the training accuracy and model performance.
We design an edge branching self-network formed by CoT unit
gated convolution
and SCSE attention mechanism to realize the information complementarity of edge and depth features. We construct a joint edge enhancement loss function called BE-loss with constraints to enhance the attention of the model to boundary information. On this basis
we construct a cultivated land information extraction model
that is
BECU-net
by combining the EfficientNet backbone network and U-frame. In the multi-feature input layer of this model
the index and texture features of the preprocessed data are pre-extracted
the input structure is adjusted
and the feature expression ability of the network is improved.
The extraction accuracy of cultivated land is 94.13%
and the F1-score is 95.17%. Compared with PANet
the extraction accuracy increased by 15.01%
and the F1-score improved by 7.93%. Compared with DeeplabV3+ network
the extraction accuracy is enhanced by 2.03%
and the F1-score is increased by 1.15%. The edge of cultivated land extracted by BECU-Net model is clear
and it is close to the real edge shape of cultivated land. Few holes and islands are observed. The extracted large parcels are not missing
and the edges and corners are sharp. The extracted small parcels have clear outlines and small deformation. At various gaps and complex edges
the extraction effect of GID dataset is significantly improved compared with that of the five other models. The effect is significant when used for edge extraction
The sawtooth and cavity phenomena of cultivated land patches are effectively restrained as well.
(1) The input layer of network structure with multiple features
including exponential features and texture features
can effectively reflect the characteristics of cultivated land. (2) The edge branch subnetwork focuses on processing the shape information to better identify the boundary details in the cultivated land image. Its edge features complement the depth features of the Efficient encoder
and they can be cascaded to fully utilize the shallow details. (3) The improved combined loss function called BE-Loss with regular term solves the problem of unbalanced training sample categories and non-edge pixel-dominated loss function. Overall
the algorithm in this study provides a technical reference for further solving the problem of fuzzy boundaries when extracting cultivated land information. It also offers theoretical support for the accurate division of complex boundaries.
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