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Fusion of multispectral imagery and LiDAR data for roofing materials and roofing surface conditions assessment
International Journal of Remote Sensing ( IF 3.4 ) Pub Date : 2020-06-30
Masayu Norman, Helmi Zulhaidi Mohd Shafri, Shattri Mansor, Badronnisa Yusuf, Nurul Ain Wahida Mohd Radzali

Assessment of rooftop rainwater harvesting (RRWH) quality and suitability requires detail and reliable information on roofs. Characterization of roof surface conditions affects the quality of harvested rainwater. Nevertheless, the implementation of the system requires improvement in terms of the roof detection techniques to ensure the roof of the building is selected appropriately. Thus, the classification techniques need to be optimized to detect roof materials and roof surface conditions (new or old) with high accuracy. This study aimed to produce high precision detailed roof materials and roof surface conditions map with using high-resolution remote sensing imagery, WorldView-3 (WV3) and light detection and ranging (LiDAR) data. Three different fusion methods; layer stacking (LS), Gram-Schmidt (GS) and principal components spectral sharpening (PCSS) were explored and their performances were compared to improve the spatial and spectral richness of the image. Subsequently, the roof materials and roof surface conditions classes which include old concrete, new concrete, old metal, new metal, old asbestos and new asbestos had been discriminated by employing support vector machine (SVM) and the rule-based technique known as a decision tree (DT). Generally, generated rule-sets present a higher overall accuracy with 87%, 72% and 66% for LS, GS and PCSS, respectively. For SVM classifier, the maximum accuracy recorded for LS, PCSS and GS were 70%, 63% and 43% respectively. Therefore, rule-based classification via LS fusion technique was utilized to identify suitable rooftops for the development of harvested rainwater system in the urban area.

Findings indicate that the degradation status of a roof in heterogenous urban environments could be determined from satellite observation and the quality of roof-based harvested rainwater affected by roofing materials and roofing surface conditions can be analysed effectively.



中文翻译:

融合多光谱图像和LiDAR数据进行屋顶材料和屋顶表面状况评估

评估屋顶雨水收集(RRWH)的质量和适用性需要有关屋顶的详细和可靠的信息。屋顶表面状况的表征会影响收集到的雨水的质量。不过,该制度的实施,需要在屋顶检测技术方面的改进,以确保建筑物的屋顶进行适当选择。因此,需要优化分类技术,以高精度检测屋顶材料和屋顶表面状况(新的或旧的)。这项研究旨在使用高分辨率遥感影像,WorldView-3(WV3)和光检测和测距(LiDAR)数据生成高精度的详细屋顶材料和屋顶表面状况图。三种不同的融合方法;层堆叠(LS),探索了Gram-Schmidt(GS)和主成分光谱锐化(PCSS),并比较了它们的性能以改善图像的空间和光谱丰富度。随后,通过采用支持向量机(SVM)和基于规则的技术(称为决策)来区分包括旧混凝土,新混凝土,旧金属,新金属,旧石棉和新石棉在内的屋顶材料和屋顶表面状况类别。树(DT)。通常,对于LS,GS和PCSS,生成的规则集具有更高的总体准确性,分别为87%,72%和66%。对于SVM分类器,LS,PCSS和GS记录的最大准确性分别为70%,63%和43%。因此,

研究结果表明,可以通过卫星观测来确定异质城市环境中屋顶的退化状态,并且可以有效地分析受屋顶材料和屋顶表面状况影响的屋顶收集雨水的质量。

更新日期:2020-06-30
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