地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (11): 2073-2085.doi: 10.12082/dqxxkx.2021.210365
储国中1,2,3(), 李蒙蒙1,2,3,*(
), 汪小钦1,2,3
收稿日期:
2021-07-01
修回日期:
2021-08-26
出版日期:
2021-11-25
发布日期:
2022-01-25
通讯作者:
*李蒙蒙(1988— ),男,山东临沂人,博士,助理研究员,研究方向为高分辨率遥感图像智能处理、机器学习、城市土地利用分类。E-mail: mli@fzu.edu.cn作者简介:
储国中(1997— ),男,安徽安庆人,硕士生,研究方向为遥感技术与应用。 E-mail: N195520004@fzu.edu.cn
基金资助:
CHU Guozhong1,2,3(), LI Mengmeng1,2,3,*(
), WANG Xiaoqin1,2,3
Received:
2021-07-01
Revised:
2021-08-26
Online:
2021-11-25
Published:
2022-01-25
Contact:
* LI Mengmeng, E-mail: mli@fzu.edu.cnSupported by:
摘要:
城市区域建筑类型信息在城市功能区识别、城市环境变量反演等应用领域具有重要作用。本文提出一种融合高分辨率遥感影像高度特征的多尺度城市建筑类型分类方法。首先利用语义分割模型识别高分辨影像中建筑和阴影对象;然后借助建筑对象及其阴影信息在卫星成像时的几何关系估算建筑高度;最后基于多尺度图像分析思想,提取一系列表征建筑对象的高度、空间结构、几何等多尺度特征,利用机器学习方法进行建筑类型分类,并进一步分析不同粒度的建筑类型分析单元对分类结果的影响。选取福州市主城区国产高分二号高分辨率影像进行实验验证。结果表明:① 基于所提方法的建筑类型分类总体精度达到82.98%, kappa系数为0.77,分类精度优于本文中未加入高度信息的分类方法和单一尺度分类方法;② 引入高度特征有效提高了中低层居民楼和高层商住两用建筑类型的分类精度,较未加入高度特征的分类结果,总体精度提高了11.28%;③ 融合多个尺度的图像特征可有效减少粘连建筑误分为密集型建筑的情况,较单一尺度分类方法,总体精度提高了2.77%。在精细的数字表面模型数据缺失下,利用高分辨影像阴影信息可为建筑物高度估计提供一种有效的策略,提高城市建筑类型分类精度。此外,融合多粒度图像特征可提升城市区域复杂建筑类型的表征能力,进而提高分类精度。
储国中, 李蒙蒙, 汪小钦. 融合高度特征的高分遥感影像多尺度城市建筑类型分类[J]. 地球信息科学学报, 2021, 23(11): 2073-2085.DOI:10.12082/dqxxkx.2021.210365
CHU Guozhong, LI Mengmeng, WANG Xiaoqin. Integrating Height Features for Multi-scale Urban Building Type Classification from High-Resolution Remote Sensing Images[J]. Journal of Geo-information Science, 2021, 23(11): 2073-2085.DOI:10.12082/dqxxkx.2021.210365
Tab. 1
Demonstration of building types
建筑类型 | 影像示例 | 描述 |
---|---|---|
密集型建筑(B1) | | 主要指一群邻接紧密的建筑群,在影像上难以描绘单个建筑对象,如老城区建筑、棚户区等,通常该类型建筑周边开放空间和植被覆盖密度较低 |
中低层居民楼(B2) | | 主要指中低层居民楼,在影像上通常具有相似的形状和大小,空间排列较规则,较密集型建筑具有较高的植被覆盖密度和开放空间 |
高层商住两用(B3) | | 主要指高层居民楼和高层商业楼,在影像上和中低层居民楼有相似的视觉效果,但楼层较高,往往邻接着完整规则的阴影 |
商业综合体(B4) | | 主要指用于商业服务或办公的复合型建筑,该类型建筑往往形状不规则、高低错落,和居民楼相比面积较大,周围存在更多的开放空间 |
工厂型建筑(B5) | | 主要指用于工业或仓库存储的工厂,较居民楼有更大的面积;与商业型建筑相比,建筑形状更规则,光谱响应更均匀 |
表2
建筑提取精度评价指标
评价指标 | 计算公式 | 编号 | 描述 |
---|---|---|---|
总体精度(OA) | | (8) | TP(True Positive)是分类正确的类; FP(False Positive)是被错分为正类的负类; TN(True Negative)是分类准确的负类; FN(False Negative)是被错分为负类的正类; 如下: |
交并比(IoU) | | (9) | |
召回率(Recall) | | (10) | |
准确率(Precision) | | (11) | |
F1分数(F1 Score) | | (12) | |
全局过分误差(GOC) | | (13) | |
全局欠分误差(GUC) | | (14) | |
全局总体误差(GTC) | | (15) |
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