李夏1，李 媛2，張 博3

　　(1.水利部信息中心，北京 100053;

　　2.北京化工大學化學工程學院，北京 100029;

　　3.航天科工集團智慧產業發展有限公司，北京 100854)

　　摘要：生產建設項目地塊數量多而分布分散，當前開展自動化遙感提取生產建設項目地塊的研究較少，仍主要采用人工目視解譯遙感影像的方法進行提取，存在效率低、成本高、穩定性差等問題。基于高分一號(GF-1)遙感影像，分析歸納不同類型和階段生產建設項目組成地物的光譜、形狀和空間特征，將防塵網確定為在建生產建設項目檢測的特征地物，比較分析不同場景下DeepLabv3+和PSPNet 2種深度學習方法對防塵網的提取結果。研究結果表明：不同難易程度場景下，DeepLabv3+模型的識別結果均明顯優于PSPNet模型，從而為在建生產建設項目防塵網的遙感監管提供一種新模式。快速獲取生產建設項目地塊的位置和邊界信息，對提升水土保持監管效率、保護區域水土資源具有重要意義。

　　關鍵詞：圖像融合;卷積神經網絡;DeepLabv3+;PSPNet;自動識別;適用性;防塵網

　　Feasibility study of DeepLabv3+ and PSPNet algorithms for automatic identification of dust screens

　　LI Xia1，LI Yuan2，ZHANG Bo3

　　(1. Information Center, Ministry of Water Resources, Beijing 100053, China;

　　2. College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China;

　　3. Smart Industry Development of China Aerospace Science and Industry Co., Ltd.，Beijing 100854, China)

　　Abstract: The sites for production and construction projects are numerous and scattered. At present, there is few research on automatic remote sensing extraction of sites for production and construction projects. Manual visual interpretation of remote sensing images is still the main method for extraction, which has problems such as low efficiency, high cost, poor stability. Based on GF-1 satellite images, this paper analyzes and summarizes the spectral, shape and spatial features of different types and stages of production and construction sites. Dust screens are selected for identification of production and construction projects, and extraction results of the dust screens by DeepLabv3+ and PSPNet in different scenarios are compared and analyzed. The research results show that identification results of the DeepLabv3+ model are significantly better than that of the PSPNet model in all scenarios, which provides a new model for remote sensing supervision on dust screens of production and construction projects. It is of great significance to quickly obtain the location and boundary information of production and construction project sites for improving the supervision efficiency of soil and water conservation and protecting regional water and soil resources.

　　Key word：image fusion;convolution neural network;DeepLabv3+;PSPNet;automatic identification;feasibility;dust screens