Analysis of Built-Up Classes in Urbanised Zones Using Radar Images

IF 1 Q3 GEOGRAPHY
Joanna Pluto-Kossakowska, Joanna Giczan
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引用次数: 0

Abstract

Abstract This paper presents the results of a study to determine the potential of radar imaging to detect classes of built-up areas defined in the Urban Atlas (UA) spatial database. The classes are distinguished by function and building density. In addition to the reflectance value itself, characteristics such as building density or spatial layout can improve the identification of these classes. In order to increase the classification possibilities and better exploit the potential of radar imagery, a grey-level co-occurrence matrix (GLCM) was generated to analyse the texture of built-up classes. Two types of synthetic-aperture radar (SAR) images from different sensors were used as test data: Sentinel-1 and ICEYE, which were selected for their different setup configurations and parameters. Classification was carried out using the Random Forests (RF) and Minimum Distance (MD) methods. The use of the MD classifier resulted in an overall accuracy of 64% and 51% for Sentinel-1 and ICEYE, respectively. In ICEYE, individual objects (e.g. buildings) are better recognised than classes defined by their function or density, as in UA classes. Sentinel-1 performed better than ICEYE, with its texture images better complementing the features of urban area classes. This remains a significant challenge due to the complexity of urban areas in defining and characterising urban area classes. Automatic acquisition of training fields directly from UA is problematic and it is therefore advisable to independently obtain reference data for built-up area categories.
利用雷达图像分析城市化地区建成区
摘要本文介绍了一项研究的结果,该研究确定了雷达成像在城市地图集(UA)空间数据库中确定的建成区类别的潜力。这些类别根据功能和建筑密度来区分。除了反射率值本身外,建筑密度或空间布局等特征也可以提高对这些类别的识别。为了增加分类的可能性,更好地利用雷达图像的潜力,生成了灰度共生矩阵(GLCM)来分析建筑类的纹理。采用不同传感器的两种合成孔径雷达(SAR)图像作为测试数据:Sentinel-1和ICEYE,选择了不同的设置配置和参数。采用随机森林(RF)和最小距离(MD)方法进行分类。使用MD分类器,Sentinel-1和ICEYE的总体准确率分别为64%和51%。在ICEYE中,单个对象(例如建筑物)比由其功能或密度定义的类(如UA类)更容易识别。Sentinel-1的性能优于ICEYE,其纹理图像更好地补充了城区类的特征。这仍然是一项重大挑战,因为城市地区在定义和描述城市地区类别方面很复杂。直接从UA自动获取训练场地是有问题的,因此建议独立获取建成区类别的参考数据。
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来源期刊
CiteScore
2.00
自引率
10.00%
发文量
0
审稿时长
12 weeks
期刊介绍: Quaestiones Geographicae was established in 1974 as an annual journal of the Institute of Geography, Adam Mickiewicz University, Poznań, Poland. Its founder and first editor was Professor Stefan Kozarski. Initially the scope of the journal covered issues in both physical and socio-economic geography; since 1982, exclusively physical geography. In 2006 there appeared the idea of a return to the original conception of the journal, although in a somewhat modified organisational form. Quaestiones Geographicae publishes research results of wide interest in the following fields: •physical geography, •economic and human geography, •spatial management and planning,
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