摘要
土地覆盖分类是全球变化研究的核心,精准分类是开展土地覆盖分类变化研究的基础。基于此,本文以Deeplab-v3+模型为基础,在编码阶段改进ASPP模型,并采用Xception-ResNet结合的网络结构,解码阶段引入跃层特征融合优化模块,得到改进后的Deeplab-v3+模型,对研究区进行土地覆盖分类提取。结果表明:改进后的模型分类精度为82.93%,训练速度为7 h 22 min,相比原始模型分别提高了4%,模型训练速度提升了25%。综上可知,改进后的Deeplab-v3+模型可以实现快速且高精度的土地覆盖分类,可为土地覆盖分类研究提供技术支持。
Land cover classification is the core of global change research,and accurate classification is the basis of land cover classification change research.Based on this,based on the Deeplab-v3+model,this paper improves the ASPP model in the coding stage,adopts the network structure combined with Xception-ResNet,introduces the thermocline feature fusion optimization module in the decoding stage,obtains the improved Deeplab-v3+model,and extracts the land cover classification of the study area.The results show that the classification accuracy of the improved model is 82.93%and the training speed is 7 h 22 min,which is 4%higher than the original model and 25%faster than the original model.To sum up,the improved Deeplab-v3+model proposed in this paper can realize fast and high-precision land cover classification and provide technical support for land cover classification research.
作者
高鹏远
GAO Pengyuan(Hebei Province Bureau of Coal Geology Geological,Xingtai 054000,China)
出处
《测绘与空间地理信息》
2024年第3期150-152,共3页
Geomatics & Spatial Information Technology
关键词
深度学习
语义分割
高分二号
土地分类
deep learning
semantic segmentation
GF-2
land classification
