多尺度特征融合的DeepLabv3+遥感监测技术在自然资源领域农村乱占耕地建房综合整治中的应用
Multi-scale feature fusion DeepLabv3+in remote sensing monitoring for comprehensive management of illegal rural construction on cultivated land
摘要
[目的]为落实农村乱占耕地建房整治政策要求,本文提出了一种基于多尺度特征融合的DeepLabv3+遥感监测方法,实现耕地上建筑物的自动提取与疑似违法图斑识别.[方法]在编码器部分于ResNet50 骨干网络各阶段中嵌入高效通道注意力(ECA)模块,增强通道特征判别能力;引入特征金字塔网络(FPN)模块对多尺度特征进行侧向连接与逐层融合,使高层语义信息与浅层空间细节充分结合;调整ASPP模块空洞率以缩小感受野范围,提升对小尺度建筑物的检测敏感性.[结果]在WHU-Mix数据子集上,IoU达80.13%、F1达88.97%,优于主流语义分割模型;利用河南一号卫星影像验证了本文方法在耕地非农化监测中建筑物提取任务上的可行性.[结论]本文方法在耕地"非农化"监测场景下具有较好的应用前景,可为基层自然资源执法巡查提供技术支撑.
[Purposes]Monitoring illegal rural construction on cultivated land remains a critical challenge due to land-cover heterogeneity and building scale variability.This study proposes an enhanced DeepLabv3+framework with multi-scale feature fusion for accurate building extraction and suspected illegal patch identification.[Methods]Efficient channel attention(ECA)modules are embedded into the ResNet-50 encoder to recalibrate channel responses with negligible overhead.A feature pyramid network(FPN)performs top-down semantic propagation and lateral aggregation,enabling effective fusion of high-level semantics with spatial details.The atrous rates in ASPP are reparameterized to align the receptive field with small rural buildings.[Findings]On a subset of the WHU-Mix dataset,the method achieves 80.13%IoU and 88.97%F1-score,outperforming state-of-the-art baselines.Cross-scene validation using Henan-1 satellite imagery confirms the framework's robustness in cultivated land non-agriculturalization monitoring.[Conclusions]The proposed approach provides a reliable solution for building extraction,supporting natural resource supervision and enforcement at the grassroots level.
年，卷(期)：
2026, (9)
分类号：
P237
机标分类号：
X831
TP79
S127
在线出版日期：
2026年10月28日
基金项目：
2025-13:河南省自然资源科技创新揭榜挂帅项目，
