A method for extraction of Shiyan terraced fields in Shexian County from remote sensing images based on improved DeepLabv3+network
Terraces,a critical component of sustainable dryland agriculture in Northern China,serve dual purposes in agricultural production and soil-water conservation.They play a crucial role in land use optimization and food security policy implementation.Distinguishing terraced fields from traditional farmland is essential due to their widespread distribution across complex topographies.Additionally,the large scale and dense distribution of dryland terraces present challenges in accurately and efficiently extracting terrace plots that adapt to terrain variations.To address these issues,this study introduces an enhanced model based on DeepLabv3+,termed DeepLabv3+.The research focused on two main aspects.First,we constructed a semantic segmentation dataset for dryland terraces using hyperspectral image data.This data was then clipped with ArcGIS and annotated with LabelMe through visual interpretation.Data augmentation techniques were subsequently applied to enhance the dataset,laying a solid foundation for model training.Second,we propose the DeepLabv3+semantic segmentation network,featuring a lightweight backbone feature network designed to extract terrace features of varying shapes and scales.We also adjusted the expansion rate of the original ASPP to effectively capture larger receptive fields and contextual information.Furthermore,the ECA module is integrated to help the network focus on key small target boundary details.Improved Deeplabv3+has a recall of 79.13％in the terrace image extraction test,which is 2.03％and 1.52％compared with HRNet and PSPNet,respectively;the MIoU is improved to 70.63％,and the accuracy is 91.34％;and the number of model parameters is reduced about 20 times compared with the U-Net model.The improved algorithm not only reduces the dependence on hardware resources for model training under the premise of ensuring 70％accuracy,thus balancing the remote sensing terrace detection accuracy and speed.The improved Deeplabv3+model consistently outperformed the others.Experimental results demonstrate that the proposed model maintains high accuracy in resource-limited or computationally constrained environments,balancing precision and efficiency in large-scale remote sensing detection of terraced fields.By incorporating an attention mechanism,this model enhances target feature recognition accuracy,providing a theoretical foundation and technical reference for land resource management and planning.It facilitates precise terraced field detection and geographic condition surveys.Utilizing remote sensing technology for terraced field distribution detection is crucial for their construction and preservation.
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