摘要
针对基于深度神经网络的高分辨率遥感影像建筑物提取算法中将建筑物提取视为二分类问题(即将遥感影像中的像素点划分为建筑物与非建筑两类)而无法区分建筑物个体的局限性,将基于Xception module改进的U-Net深度神经网络方法与多任务学习方法相结合进行建筑物实例分割,在获取建筑物二分类结果的同时,区分不同建筑物个体,并选择Inria航空影像数据集对该方法进行验证。结果表明,在高分辨率遥感影像的建筑物二分类提取方面,基于Xception module改进的U-Net方法明显优于U-Net方法,提取精度升高1.4%;结合多任务学习的深度神经网络方法不仅能够实现建筑物的实例分割,而且可将二分类建筑物的提取精度提升约0.5%。
At present,building extraction from high-resolution remote sensing images using deep neural network is viewed as a binary classification problem,which divides the pixels into two categories,building and nonbuilding,but it cannot distinguish individual buildings.To solve this problem,the U-Net modified with Xception module and multitask learning are combined to apply to the instance segmentation of buildings,which both acquires the binary classification and distinguishes the individual buildings.Inria aerial imagery is used as the research dataset to validate the algorithm.The results show that the binary classification performance of U-Net modified with Xception outperforms U-Net by about 1.4%.The multitask driven deep neural network not only accomplishes the instance segmentation of buildings,but also improves the accuracy by about 0.5%.
作者
惠健
秦其明
许伟
隋娟
HUI Jian;QIN Qiming;XU Wei;SUI Juan(Institute of Remote Sensing and Geographic Information System,School of Earth and Space Sciences,Peking University,Beijing 100871;Beijing Key Lab of Spatial Information Integration and 3S Application,Beijing 100871;Geographic Information System Technology Innovation Center,Ministry of Natural Resources,Beijing 100871)
出处
《北京大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2019年第6期1067-1077,共11页
Acta Scientiarum Naturalium Universitatis Pekinensis
基金
国家重点研发计划(2017YFB0503905)资助
关键词
多任务学习
建筑物提取
深度神经网络
实例分割
multitask learning
building extraction
deep neural network
instance segmentation
