河北工业科技2025，Vol.42Issue(5)：401-411,11.DOI:10.7535/hbgykj.2025yx05001
基于改进Deeplabv3+的遥感滑坡分割提取模型
Remote sensing landslide segmentation and extraction model based on improved Deeplabv3+
摘要
Abstract
In order to address the limitations of traditional high-resolution landslide image segmentation methods in handling details and blurred boundaries,an enhanced Deeplabv3+model(SCPD-Deeplabv3+)was proposed,which integrated Swin Transformer network,convolutional block attention module(CBAM),position attention feature pyramid network(PA-FPN),and multi-layer convolutional decoder.Firstly,the baseline model Deeplabv3+was improved by adopting Swin Transformer as the backbone network,introducing CBAM into the atrous spatial pyramid pooling(ASPP)module of Deeplabv3+,integrating PA-FPN into the decoder,and adding more convolutional layers during the upsampling process.Secondly,the improved Deeplabv3+model was trained.Finally,the high-resolution landslide image test set was fed into the trained SCPD-Deeplabv3+model for ablation experiments to analyze the role of each module,and comparisons with mainstream models such as UNet,proportional-integral-derivative network(PIDNet),and real-time transformer(RTFormer)for semantic segmentation were performed through quantitative evaluation and visualization.The results show that SCPD-Deeplabv3+achieves an average intersection over union of 90.18%,precision of 93.57%,recall of 94.47%,and F1-score of 93.58%,respectively,which are improved by 3.39 percentage points,1.45 percentage points,3.90 percentage points,and 3.51 percentage points compared with the unmodified model.The proposed method effectively enhances the segmentation accuracy and detail restoration capability for landslide areas,providing a reliable technical means for remote sensing landslide monitoring and disaster assessment.关键词
计算机图像处理/滑坡分割/Deeplabv3+/Swin TransformerKey words
computer image processing/landslide segmentation/Deeplabv3+/Swin Transformer分类
信息技术与安全科学引用本文复制引用
王建霞,郭玉凤,杨春金,张晓明..基于改进Deeplabv3+的遥感滑坡分割提取模型[J].河北工业科技,2025,42(5):401-411,11.基金项目
河北省自然科学基金(F2022208002) （F2022208002）
