Clasificación de imágenes urbanas aéreas: Comparación entre descriptores de bajo nivel y aprendizaje profundo

This paper presents a comparison between different low-semantic descriptive algorithms coupled with a support vector machine and the deep learning algorithm, for the task of recognition and classification of aerial images. For this task, a database composed of 1200 images is used to fulfill the supervised trainings. The objective consists on classifying images in six categories that are commonly found on urban areas, in order to be used in any part of the world. The results show that with 150 samples of each class, the deep learning algorithm is capable of classifying images of avenues, buildings, industries, natural areas, residential areas and water bodies with an 87% of accuracy. Experimental results also prove that the labeled images as industry and buildings are the most complex ones to distinguish among these two classes, both for low-level descriptors and deep learning techniques.