Abstract
Abstract. In light of the ongoing urban sprawl reported in recent studies, accurate urban mapping is essential for assessing current status and evolve new policies, to overcome various social, environmental, and economic consequence. Imagery and LiDAR data integration densifies remotely sensed data with radiometric and geometric characteristics, respectively, for a precise segregation of different urban features. This study integrated aerial and LiDAR images using point primitives, which were obtained from running the Phase Congruency model as an image filter to detect edges and corner. The main objective is to study the effect of applying the filter at different spatial resolutions on the registration accuracy and processing time. The detected edge/corner points that are mutual in both datasets, were identified as candidate points. The Shape Context Descriptor method paired-up candidate points as final points based on a minimum correlation of 95%. Affine, second and third order polynomials, in addition to the Direct Linear Transformation models were applied for the image registration process using the two sets of final points. The models were solved using Least Squares adjustments, and validated by a set of 55 checkpoints. It was observed that with the decrease in spatial resolution, on one hand, the registration accuracy did not significantly vary. However, the consistency of the model development and model validation accuracies were enhanced, especially with the third order polynomial model. On the other hand, the number of candidate points decreased; consequently, the processing time significantly declined. The 3D LiDAR points were visualised based on the Red, Green, and Blue radiometric values that were inherited from the aerial photo. The qualitative inspection was very satisfactory, especially when examining the scene’s tiny details. In spite of the interactivity in determining the candidate points, the proposed procedure overcomes the dissimilarity between datasets in terms of acquisition technique and time, and widens the tolerance of accepting points as candidates by including points that are not traditionally considered (i.e. road intersections).
Highlights
Our world is encountering the largest wave of urbanization
Urban sprawl has begun to be a serious challenge for governments
Working out its consequences requires the integration of remotely sensed data obtained by different sensors, for more accurate classification of different urban features
Summary
Our world is encountering the largest wave of urbanization. A decade ago, only two out of ten settled in urban communities. The United Nations in its 2018’s report stated that over 80% of the population in North America lives in urban settlements (UN, 2015) This urban sprawl is a challenge for the concerned authorities to overcome deterioration in available resources and services. Accurate and precise detection of different urban morphologies is essential for evaluating current situations, and developing effective plans to tackle anticipated urban expansion in the future. This magnifies the necessity of smart cities as a technological way out of this confrontation, where digital remotely sensed data can be collected from different sensors, integrated, processed, and analyzed (Li et al, 2013)
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