Automatic building change detection on aerial images using convolutional neural networks and handcrafted features

Diego Alonso Javier Quispe, Jose Alfredo Sulla Torres

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

Resumen

In this article, we present a new framework to solve the task of building change detection, making use of a convolutional neural network (CNN) for the building detection step, and a set of handcrafted features extraction for the change detection. The buildings are extracted using the method called Mask R-CNN which is a neural network used for object-based instance segmentation and has been tested in different case studies to segment different types of objects obtaining good results. The buildings are detected in bitemporal images, where three different comparison metrics MSE, PSNR and SSIM are used to differentiate if there are changes in buildings, we used this metrics in the Hue, Saturation and Brightness representation of the image. Finally the characteristics are classified by two algorithms, Support Vector Machine and Random Forest, so that both results can be compared. The experiments were performed in a large dataset called WHU building dataset, which contains very high-resolution (VHR) aerial images. The results obtained are comparable to those of the state of the art.

Idioma originalInglés
Páginas (desde-hasta)679-684
Número de páginas6
PublicaciónInternational Journal of Advanced Computer Science and Applications
Volumen11
N.º6
DOI
EstadoPublicada - 2020

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