Building detection from orthophotos using a machine learning approach: An empirical study on image segmentation and descriptors Articles uri icon

authors

  • Dornaika, Fadi
  • MOUJAHID MOUJAHID, ABDELMALIK
  • El Merabet, Youssef
  • Ruichek, Yassine Ssine

publication date

  • January 2016

start page

  • 130

end page

  • 142

issue

  • 1

volume

  • 58

International Standard Serial Number (ISSN)

  • 0957-4174

Electronic International Standard Serial Number (EISSN)

  • 1873-6793

abstract

  • Building detection from aerial images has many applications in fields like urban planning, real-estate management, and disaster relief. In the last two decades, a large variety of methods on automatic building detection have been proposed in the remote sensing literature. Many of these approaches make use of local features to classify each pixel or segment to an object label, therefore involving an extra step to fuse pixelwise decisions. This paper presents a generic framework that exploits recent advances in image segmentation and region descriptors extraction for the automatic and accurate detection of buildings on aerial orthophotos. The proposed solution is supervised in the sense that appearances of buildings are learnt from examples. For the first time in the context of building detection, we use the matrix covariance descriptor, which proves to be very informative and compact. Moreover, we introduce a principled evaluation that allows selecting the best pair segmentation algorithm-region descriptor for the task of building detection. Finally, we provide a performance evaluation at pixel level using different classifiers. This evaluation is conducted over 200 buildings using different segmentation algorithms and descriptors. The performance analysis quantifies the quality of both the image segmentation and the descriptor used. The proposed approach presents several advantages in terms of scalability, suitability and simplicity with respect to the existing methods. Furthermore, the proposed scheme (detection chain and evaluation) can be deployed for detecting multiple object categories that are present in images and can be used by intelligent systems requiring scene perception and parsing such as intelligent unmanned aerial vehicle navigation and automatic 3D city modeling. © 2016 Elsevier Ltd. All rights reserved.

keywords

  • air navigation; artificial intelligence; buildings; classifiers; covariance matrix; disaster prevention; image processing; intelligent systems; learning systems; object detection; office buildings; pixels; quality control; remote sensing; supervised learning; automatic building detection; covariance descriptor; image descriptors; machine learning approaches; orthophotos; performance analysis; real estate managements; segmentation algorithms; image segmentation