文献类型：期刊文献

英文题名：A lightweight Deeplab V3+network integrating deep transitive transfer learning and attention mechanism for burned area identification

作者：Liu, Lizhi[1,2,3] Guo, Ying[2,3] Chen, Erxue[2,3] Li, Zengyuan[2,3] Li, Yu[4] Liu, Yang[4] Zhang, Qiang[5] Wang, Bing[5]

第一作者：Liu, Lizhi

通信作者：Guo, Y[1];Guo, Y[2]

机构：[1]Tarim Univ, Coll Hort & Forestry, Alar 843300, Xinjiang, Peoples R China;[2]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[3]Chinese Acad Forestry, Key Lab Forestry Remote Sensing & Informat Syst, Natl Forestry & Grassland Adm, Beijing 100091, Peoples R China;[4]Liaoning Tech Univ, Sch Geomat, Fuxin 123000, Peoples R China;[5]Inner Mongolia Agr Univ, Coll Forestry, Hohhot, Peoples R China

年份：2025

卷号：15

期号：1

外文期刊名：SCIENTIFIC REPORTS

收录：;Scopus(收录号：2-s2.0-105004454436);WOS:【SCI-EXPANDED(收录号:WOS:001484399900025)】；

基金：This research was funded by the Special Funds for Basic Research Operating Costs of Central Public Welfare Research Institutes "Research on Tree Species Type Classification Methods with Differen-tiated Characteristics of Multiple Optical Sensors" (Grant: CAFYBB2022SY030), the 14th Five-Year National Key Research and Development Program "Multi-Source Remote Sensing Monitoring Technology for Vegetation Coverage Types and Forest Quantitative Parameters" (Grant: 2023YFD2201703), Automatic extraction technology for vegetation cover types in multi-source remote sensing areas (2023YFD220170301), President Fund of Tarim University (Natural Science Program), the 2023 Inner Mongolia Autonomous Region Postgraduate Scientific Research Innovation Project (Grant: B20231092Z), the National Science and Technology Major Project of China's High-Resolution Earth Observation System (Grant: 21-Y20B01-9001-19/22), the Science and Technology Plan Project of Inner Mongolia, China (Forest Ecosystem National Observation and Research Station of Greater Khingan Mountains in Inner Mongolia) and the National Science Foundation of China (Grant: 32260389).

语种：英文

摘要：Complete and accurate burned area map data are needed to document spatial and temporal patterns of fires, to quantify their drivers, and to assess the impacts on human and natural systems. To achieve the the purpose of identifying burned area accurately and efficiency from remote sensing images, a lightweight deep learning model is proposed based on Deeplab V3 + , which employs the combination of attention mechanism and deep transitive transfer learning (DTTL) strategy. The lightweight MobileNet V2 network integrated with Convolutional Block Attention Module (CBAM) is designed as the backbone network to replace the traditional time-consuming Xception of Deeplab V3 +. The attention mechanism is introduced to enhance the recognition ability of the proposed deep learning model, and the deep transitive transfer learning strategy is adopted to solve the problem of incorrect identification of the burned area and discontinuous edge details caused by insufficient sample size during the extraction process. For the process of DTTL, the improved Deeplab V3 + network was first pre-trained on ImageNet. Sequentially, WorldView-2 and the Sentinel-2 dataset were employed to train the proposed network based on the ImageNet pre-trained weights. Experiments were conducted to extract burned area from remote sensing images based on the trained model, and the results show that the proposed methodology can improve extraction accuracy with OA of 92.97% and Kappa of 0.819, which is higher than the comparative methods, and it can reduce the training time at the same time. We applied this methodology to identify the burned area in Western Attica region of Greece, and a satisfactory result was achieved with. OA of 93.58% and Kappa of 0.8265. This study demonstrates the effectiveness of the improved Deeplab V3 +in identifying forest burned area. which can provide valuable information for forest protection and monitoring.