Abstract
Information extracted from aerial photographs has been used for many practical applications such as urban planning, forest management, disaster relief, and climate modeling. In many cases, labeling of information in the photo is still performed by human experts, making the process slow, costly, and error-prone. This article shows how a convolutional neural network can be used to determine the location of ground control points (GCPs) in aerial photos, which significantly reduces the amount of human labor in identifying GCP locations. Two convolutional neural network (CNN) methods, sliding-window CNN with superpixel-level majority voting, and superpixel-based CNN are evaluated and analyzed. The results of the classification and segmentation show that both of these methods can quickly extract the locations of objects from aerial photographs, but only superpixel-based CNN can unambiguously locate the GCPs.
Highlights
A ERIAL image interpretation finds applications in many diverse areas including urban planning, forest management, vegetation monitoring, and climate modeling [1], [2]
We propose sliding-window convolutional neural network (CNN) with superpixel-level majority voting, which apply a simple linear iterative clustering (SLIC) and the density-based spatial clustering of applications with noise (DBSCAN) for image segmentation after classification
In the sliding-window CNN, the SLIC and majority voting are applied in the postprocessing to remove the misclassifications from the CNN results
Summary
A ERIAL image interpretation finds applications in many diverse areas including urban planning, forest management, vegetation monitoring, and climate modeling [1], [2]. Standard GPS accuracy used to geotag image is usually in the order of few meters. This error causes a systematic shifting in the processed outputs far beyond the acceptable bound, which typically ranges between 1 and 5 cm. To improve the accuracy of the results, ground control points (GCPs) are evenly distributed across the area of interest and measured using high accuracy methods such as real-time kinematic (RTK) GPS. Much of the work in identifying the locations of these GCPs in the photographs is still performed by human experts, which can be labor intensive with the explosive growth in data volumes
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