Abstract
One of the principal difficulties related to road safety management in Brazil is the lack of data on road projects, especially those on rural roads, which makes it difficult to use road safety studies and models from other countries as a reference. Updating road networks through the use of hyperspectral remote sensing images can be a good alternative. However, accurately recognizing and extracting hyperspectral images from roads has been recognized as a challenging task in the processing of hyperspectral data. In order to solve the aforementioned challenges, Hyperion hyperspectral images were combined with the Optimum Forest Path (OPF) algorithm for supervised classification of rural roads and the effectiveness of the OPF and SVM classifiers when applied to these areas was compared. Both classifiers produced reasonable results, however, the OPF algorithm outperformed SVM. The higher classification accuracy obtained by the OPF was mainly attributed to the ability to better distinguish between regions of exposed soil and unpaved roads.
Highlights
Traffic accidents in Brazil stand out in terms of magnitude, both in number of deaths and injuries as well as in their financial consequences for users and for society
In order to solve the aforementioned challenges, Hyperion hyperspectral images were combined with the Optimum Forest Path (OPF) algorithm for supervised classification of rural roads and the effectiveness of the OPF and SVM classifiers when applied to these areas was compared
The country remains far from the goal established by the United Nations (UN), which stipulates a 50% reduction in the number of victims over 10 years, beginning in 2011, and it ranks fifth among countries with the most traffic deaths, behind only India, China, the United States, and Russia
Summary
Traffic accidents in Brazil stand out in terms of magnitude, both in number of deaths and injuries as well as in their financial consequences for users and for society. Another aggravating fact is that traditional methods for updating maps have not kept pace with the increase in the number of roads caused by the country’s socioeconomic growth in recent decades, either due to the difficulty of accessing some locations, the difficulty in finding specialized technical personnel or the high cost. The idea in this manuscript is not to act directly on feature extraction, as deep learning does, but on the classification stage, and the OPF can be used with the features learned by these networks In this method, a geometric base of rural roads will initially be built from road segments extracted from the image. Techniques for grouping and reconstructing missing segments from the road network will be used
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