文章基本信息

标题：Automatic Building Change Detection on Aerial Images using Convolutional Neural Networks and Handcrafted Features
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作者：Diego Alonso Javier Quispe ; Jose Sulla-Torres
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：6
DOI：10.14569/IJACSA.2020.0110683
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Bi-temporal images; convolutional neural network (CNN); building detection; building change detection; Mask R-CNN