基于SegNet语义模型的高分辨率遥感影像农村建设用地提取  

Rural construction land extraction from high spatial resolution remote sensing image based on SegNet semantic segmentation model

作　　者：杨建宇 周振旭[1] 杜贞容 许全全 尹航 刘瑞 Yang Jianyu;Zhou Zhenxu;Du Zhenrong;Xu Quanquan;Yin Hang;Liu Rui(College of Land Science and Technology,China Agricultural University,Beijing 100083,China;Key Laboratory for Agricultural Land Quality Monitoring and Control,Ministry of Land and Resources,Beijing 100035,China)

机构地区：[1]中国农业大学土地科学与技术学院,北京100083 [2]国土资源部农用地质量与监控重点实验室,北京100035

出　　处：《农业工程学报》2019年第5期251-258,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国土资源部公益性行业科研专项(201511010-06)

摘　　要：针对传统分类算法、浅层学习算法不适用于高空间分辨率遥感影像中农村建筑物信息提取的问题,该文以河北省霸州市高空间分辨率遥感影像WorldView-2为数据源,利用182 064幅128×128像素大小的影像切片为训练样本,选取基于深度卷积神经网络的SegNet图像语义分割算法对遥感影像中的农村建筑物进行提取,并与传统分类算法中的最大似然法(maximum likelihood,ML)和ISO聚类、浅层学习算法中的支持向量机(support vector machine,SVM)和随机森林(random forest,RF)以及深层语义分割算法中的金字塔场景解析网络(pyramid scene parsing network,PSPNet)的试验结果作对比分析。研究结果表明:SegNet不仅能够高效利用高空间分辨率遥感影像中农村建筑物的光谱信息而且还能够充分利用其丰富的空间特征信息,最终形成较好的分类模型,该算法在验证样本中的分类总体精度为96.61%,Kappa系数为0.90,建筑物的F1值为0.91,其余5种分类算法的总体精度、Kappa系数、建筑物的F1值都分别在94.68%、0.83、0.87以下。该研究可以为高空间分辨率遥感影像农村建设用地提取研究提供参考。With the advancement of remote sensing technology,the high spatial resolution remote sensing image contains rich special information with a great detail.At the same time,the complexity of high spatial resolution remote sensing images also requires higher the classification technology of remote sensing images.However,in the face of high spatial resolution remote sensing image more obvious geometrical structure and the more rich texture characteristics,how to design rational system of characteristics,select the appropriate sorting algorithms to accurately and quickly grasp the number of rural land of building and its distribution status,are of great significance to balance urban and rural areas,save land,and realize sustainable development.This will help in exploring the application of deep learning model in high spatial resolution remote sensing image building extraction,and have research significance for improving the classification accuracy of high resolution remote sensing image.In this paper,the semantic segmentation model(SegNet)was used for extracting buildings.SegNet is mainly composed of encoder network,decoder network and pixel-wise classification layer.The encoder network transforms high-dimensional vectors into low-dimensional vectors,enabling low-dimensional extraction of high-dimensional features.The decoder network maps low-resolution feature maps to high spatial resolution feature maps,realizing the reconstruction of low-dimensional vectors to high-dimensional vectors.The softmax classifier separately classifies each pixel,which outputs the probability that each pixel belongs to each class.In this paper,a 3000 pixel×3000 pixel and two 2000 pixel×2000 pixel slices were taken from the global remote sensing image of Bazhou City,Hebei Province as training samples,and a 3000 pixel×3000 pixel slice was taken as the verification sample.In this paper,five comparative experiments were used to extract the buildings,including PSPNet,support vector machine,random forest,ISO clustering and maximum likelihood

关 键 词：遥感 图像分割 算法 深度学习 SegNet语义分割模型 高空间分辨率遥感影像 农村建设用地提取 

分 类 号：S127[农业科学—农业基础科学]