Fusion of multispectral imagery and LiDAR data for roofing materials and roofing surface conditions assessment

Abstract

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.

An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs

“…The burdensome requirement of a large training set limits the adoption of deep learning algorithms for roofing materials classification, because in many practical cases ground-truth datasets are relatively small and they can be exploited more efficiently with SVM [51]. Here, the achieved accuracies (overall 91%, user and producer reported in Table 3) are comparable (or outperform in some cases) with the reported experiments using similar imagery and OBIA approach [12][13][14], and also with few works exploiting convolutional neural networks but with a more limited number of material classes [9,23].…”

“…It is to be noted that very often the published tests are performed over very limited areas (typically a few square kilometers) [12][13][14], with relatively homogeneous urban texture. From the point of view of the main data sources, two approaches were proposed in the literature: the first relies only on images, which can be multispectral [8,9,15] or hyperspectral [16][17][18], and the second combines images with a digital elevation model of the urban area [14,19].…”

“…It is to be noted that very often the published tests are performed over very limited areas (typically a few square kilometers) [12][13][14], with relatively homogeneous urban texture. From the point of view of the main data sources, two approaches were proposed in the literature: the first relies only on images, which can be multispectral [8,9,15] or hyperspectral [16][17][18], and the second combines images with a digital elevation model of the urban area [14,19]. When included in the processing, the contribution of the elevation model becomes dominant especially for the identification of roof surfaces (by excluding all the other urban elements), while spectral information is used for material identification [19].…”

Mapping Roofing with Asbestos-Containing Material by Using Remote Sensing Imagery and Machine Learning-Based Image Classification: A State-of-the-Art Review

“…Several attempts have been made to address the noted challenges. Using multisource images [30,32], segmentation optimisation techniques [47], and advanced non-parametric classifiers [48] have shown good enhancement in the reviewed studies. Moreover, further research on methods such as DL-based semantic segmentation adopted in other remote sensing applications [49] could improve the ACM roof mapping process.…”

“…Remote sensors capture different numbers of spectral bands, resulting in various imagery types. In the reviewed studies of ACM roof mapping, two RSI types are often utilised as the input data: (1) hyperspectral imagery (HSI) [13,[20][21][22][23][24], which provides hundreds of narrow bands [25]; and (2) multispectral imagery (MSI) [10][11][12][26][27][28][29][30][31][32], which provides a few numbers of wider bands (e.g., blue, green, red, and near-infrared bands) and often has very high spatial resolution [33].…”