Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach
Mapping Complex Urban Land Cover from Spaceborne Imagery: The Influence of Spatial Resolution, Spectral Band Set and Classification Approach
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DOI：
10.3390/rs8020088
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发表时间：
2016-02-01
期刊：
影响因子：
5
通讯作者：
Boyd, Doreen S.
中科院分区：
文献类型：
--
作者：
Momeni, Rahman;Aplin, Paul;Boyd, Doreen S.
详细的土地覆被信息对于绘制复杂的城市环境图很有价值。最近对卫星传感器技术的增强有望提供适合用途的数据，特别是在使用当代分类方法进行处理时。我们通过比较空间分辨率、光谱波段设置和分类方法对绘制英国诺丁汉详细城市土地覆盖图的影响来评估这一承诺。一幅WorldView-2图像为一组12幅具有不同空间和光谱特征的图像提供了基础，这些图像使用三种不同的方法（最大似然法、支持向量机和基于对象的图像分析）进行分类，产生36幅输出土地覆盖图。独立评估分类准确性，并在所有配对输出（共630对）之间进行McNemar检验，以确定哪些分类存在显著差异。总体精度变化之间的ML分类30米的空间分辨率，4波段的图像和91%的OBIA分类2米的空间分辨率，8波段的图像。结果表明，在绘制复杂的城市环境时，空间分辨率显然是最具影响力的因素，现代甚高分辨率或VHR传感器在此方面具有很大的优势。然而，一些最新的传感器提供的先进的光谱能力，加上当代的分类方法（特别是支持向量机和OBIA），也可以导致显着提高映射精度。仪器和方法的持续发展在这方面提供了巨大的潜力，并意味着城市测绘的机会将继续增长。
Detailed land cover information is valuable for mapping complex urban environments. Recent enhancements to satellite sensor technology promise fit-for-purpose data, particularly when processed using contemporary classification approaches. We evaluate this promise by comparing the influence of spatial resolution, spectral band set and classification approach for mapping detailed urban land cover in Nottingham, UK. A WorldView-2 image provides the basis for a set of 12 images with varying spatial and spectral characteristics, and these are classified using three different approaches (maximum likelihood (ML), support vector machine (SVM) and object-based image analysis (OBIA)) to yield 36 output land cover maps. Classification accuracy is evaluated independently and McNemar tests are conducted between all paired outputs (630 pairs in total) to determine which classifications are significantly different. Overall accuracy varied between 35% for ML classification of 30 m spatial resolution, 4-band imagery and 91% for OBIA classification of 2 m spatial resolution, 8-band imagery. The results demonstrate that spatial resolution is clearly the most influential factor when mapping complex urban environments, and modern very high resolution or VHR sensors offer great advantage here. However, the advanced spectral capabilities provided by some recent sensors, coupled with contemporary classification approaches (especially SVMs and OBIA), can also lead to significant gains in mapping accuracy. Ongoing development in instrumentation and methodology offer huge potential here and imply that urban mapping opportunities will continue to grow.