Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Roof materials and roof surface conditions are the main factors that contribute to the determination of rooftop rainwater harvesting (RRWH) quality and quantity in an urban area. Therefore, these factors are required to be analyzed effectively. According to the previous study, the typ...
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Format: | Thesis |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | http://psasir.upm.edu.my/id/eprint/84233/1/FK%202019%20100%20-%20ir.pdf |
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Summary: | Roof materials and roof surface conditions are the main factors that contribute to the
determination of rooftop rainwater harvesting (RRWH) quality and quantity in an urban
area. Therefore, these factors are required to be analyzed effectively. According to the previous
study, the types of different roof materials and roof surface conditions that affected by aging
and weathering effects, contribute to the spectral response variability of these features.
Even though multispectral images have been used to map the roofing types previously, the
characterization of roof surface conditions remains limited because of spectral, spatial, and
textural limitations. To overcome such challenges, this study attempts to comprehend and improve the remote sensing
technology for rooftop classification using the Worldview-3 (WV-3) image and object-based image
analysis (OBIA) method. The improvement process involved segmentation, feature selection and
classification techniques.
A spatio-statistical optimization technique that combines the Taguchi statistical method
and a spatial plateau objective function (POF) was presented to improve the segmentation procedures
for building footprint extraction. The optimal scale, shape and compactness parameters of
multi-resolution segmentation have been determined and the detection accuracy was evaluated based
on receiver operating characteristics (ROC). The result shows the area under the ROC curve (AUC)
of 0.804 with p < 0.0001 at 95% confidence level.
Furthermore, a systematic feature selection approach was proposed in which search algorithms
(Ant-Search, Best First-Search and Particle Swamp Optimization (PSO) -
Search) performance were evaluated to select the most significant features. The accuracy of each algorithm was evaluated using LibSVM, Bayes network, and
Adaboost classifier. The result presents that the Ant-Search algorithm via LibSVM was determined as
the best combination with 100% accuracy.
The accuracy of classification result using OBIA is insufficient to depend on the
segmentation parameters, the selection of features, and the existence of spectrally mixed
objects. An analysis of the choice of classification techniques is also required. Therefore, the
LiDAR derived data were combined with WV-3 image using different fusion methods such as layer
stacking (LS), Gram–Schmidt (GS), and PC spectral sharpening (PCSS). Then, the classifier
(support vector machine (SVM) and data mining (DM) algorithm, decision tree (DT) were applied
on each fusion image and their accuracy were evaluated. Generally, the generated DT classification
presents a higher overall accuracy with 87%, 72%, and 66% for LS, GS, and PC Pan-Sharpening (PCSS),
respectively. Meanwhile, the DT classification using the LS approach produced the highest
overall accuracy of 87% and a kappa coefficient of 0.80.
Overall, this study offers new insights into remote sensing urban applications,
specifically for roof-based mapping through the development of systematic
improvement approach using OBIA. Interestingly, the degradation status of the roof in heterogenous
urban environments can be determined and the quality of roof-based harvested rainwater affected
by roofing materials and roofing conditions can be
analyzed effectively. |
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