264
Views
3
CrossRef citations to date
0
Altmetric
Articles

A Cognitive Viewpoint on Building Detection from Remotely Sensed Multispectral Images

ORCID Icon &

REFERENCES

  • A. O. Ok, C. Senaras and B. Yuksel. (2013). “Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery,” IEEE Trans. Geosci. Remote Sens. 51 (3), pp. 1701–1717.
  • B. Sirmacek and C. Unsalan. (2011). “A probabilistic framework to detect buildings in aerial and satellite images,” IEEE Trans. Geosci. Remote Sens. 49 (1), pp. 211–221.
  • B. Sirmacek and C. Unsalan. (2009). “Urban-area and building detection using SIFT keypoints and graph theory,” IEEE Trans. Geosci. Remote Sens. 47 (4), pp. 1156–1167.
  • H. Mayer. (1999). “Automatic object extraction from aerial imagery—a survey focusing on buildings,” Comput. Vis. Image Understand. 74 (2), pp. 138–149.
  • C. Ozgen, “Approaches for automatic urban building extraction and updating from high resolution satellite imagery,” Ph.D. dissertation, Middle East Technical Univ., Ankara, 2009.
  • T. Kim and J. P. Muller. (1999). “Development of a graph-based approach for building detection,” Image Vision Comput. 17 (1), pp. 3–14.
  • S. Krishnamachari and R. Chellappa. (1995). “Delineating buildings by grouping lines with MRFs,” IEEE Trans. Image Process. 5 (1), pp. 164–168.
  • M. Molinier, J. Laaksonen and T. Hame. (2007) “Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on self-organizing maps.” IEEE Trans. Geosci. Remote Sens. 45 (4), pp. 861–874.
  • P. Gamba, F. Dell'Acqua, G. Lisini and G. Trianni. (2007). “Improved VHR urban area mapping exploiting object boundaries,” IEEE Trans. Geosci. Remote Sens. 45 (8), pp. 2676–2682.
  • J. A. Benediktsson, M. Pesaresi and K. Amason. (2003). “Classification and feature extraction for remote sensing images from urban areas based on morphological transformations,” IEEE Trans. Geosci. Remote Sens. 41 (9), pp. 1940–1949.
  • C. Unsalan and K. L. Boyer. (2005). “A system to detect houses and residential street networks in multispectral satellite images,” Comput. Vis. Image Understand. 98 (3), pp. 423–461.
  • H. G. Akçay and S. Aksoy. (2008). “Automatic detection of geospatial objects using multiple hierarchical segmentations,” IEEE Trans. Geosci. Remote Sens. 46 (7), pp. 2097–2111.
  • M. Idrissa, V. Lacroix, A. Hincq, H. Bruynseels and O. Swartenbroekx, “SPOT5 images for urbanization detection,” in Proc. Advanced Concepts for Intelligent Vision Systems, 2004.
  • B. Sirmacek and C. Unsalan. (2009). “Urban-area and building detection using SIFT keypoints and graph theory,” IEEE Trans. Geosci. Remote Sens. 47 (4), pp. 1156–1167.
  • Z. Xiong and Y. Zhang, (2009). “A novel interest-point-matching algorithm for high-resolution satellite images,” IEEE Trans. Geosci. Remote Sens. 47 (12), pp. 4189–4200.
  • C. Senaras and F. T. Y. Vural. (2016). “A self-supervised decision fusion framework for building detection,” IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 9 (5), pp. 1780–1791.
  • C. Benedek, X. Descombes, and J. Zerubia, “Building extraction and change detection in multitemporal aerial and satellite images in a joint stochastic approach,” Ph.D. dissertation, INRIA, France, 2009.
  • K. Karantzalos and N. Paragios. (2009). “Recognition-driven two-dimensional competing priors toward automatic and accurate building detection,” IEEE Trans. Geosci. Remote Sens. 47 (1), pp. 133–144.
  • A. Katartzis and H. Sahli. (2008). “A stochastic framework for the identification of building rooftops using a single remote sensing image,” IEEE Trans. Geosci. Remote Sens. 46 (1), pp. 259–271.
  • W. Liu and V. Prinet, “Building detection from high-resolution satellite image using probability model,” in Proc. International Geoscience and Remote Sensing Symposium, vol. 6, July, 2005, p. 3888.
  • Y. Bazi and F. Melgani. (2010). “Gaussian process approach to remote sensing image classification,” IEEE Trans. Geosci. Remote Sens. 48 (1), pp. 186–197.
  • S. Bhagavathy and B. S. Manjunath. (2006). “Modeling and detection of geospatial objects using texture motifs,” IEEE Trans. Geosci. Remote Sens. 44 (12), pp. 3706–3715.
  • M. Fauvel, J. Chanussot and J. A. Benediktsson. (2006) “Decision fusion for the classification of urban remote sensing images,” IEEE Trans. Geosci. Remote Sens. 44, pp. 2828–2838.
  • J. Munoz-Mari, L. Bruzzone and G. Camps-Valls. (2007). “A support vector domain description approach to supervised classification of remote sensing images,” IEEE Trans. Geosci. Remote Sens. 45 (8), pp. 2683–2692.
  • D. Tuia, F. Pacifici, M. Kanevski and W. J. Emery. (2009). “Classification of very high spatial resolution imagery using mathematical morphology and support vector machines,” IEEE Trans. Geosci. Remote Sens. 47 (11), pp. 3866–3879.
  • C. Unsalan. (2006). “Gradient-magnitude-based support regions in structural land use classification,” IEEE Geosci. Remote Sens. Lett. 3 (4), pp. 546–550.
  • B. Sirmacek and C. Unsalan. (2010). “Urban area detection using local features and spatial voting,” IEEE Geosci. Remote Sens. Lett. 7 (1), pp. 146–150.
  • C. Unsalan and K. L. Boyer. (2004). “Classifying land development in high resolution panchromatic satellite images using straight line statistics,” IEEE Trans. Geosci. Remote Sens. 42 (4), pp. 907–919.
  • P. Zhong and R. Wang. (2007). “A multiple conditional random fields ensemble model for urban area detection in remote sensing optical images,” IEEE Trans. Geosci. Remote Sens. 45 (12), pp. 3978–3988.
  • R. E. Clark, D. Feldon, J. J. Van Merrienboer, K. Yates and S. Early, Cognitive Task Analysis. Handbook of Research on Educational Communications and Technology. New York, NY: Routledge, 2008, pp. 577–593.
  • E. B. Goldstein, Cognitive Psychology: Connecting Mind, Research and Everyday Experience. Belmont, CA: Nelson Education, 2014.
  • R. J. Sternberg and K. Sternberg, Cognitive Psychology. Belmont, CA: Nelson Education, 2016.
  • A. Schoeke and T. Bittlin, Cognitive Psychology and Cognitive Neuroscience, Books4x Company, 2007, ISBN 1449986438. Available: file:///E:/Academics/DATA/PhD/BOOKS/Cognitive_Psychology_and_Cognitive_Neuroscience.pdf
  • V. Chiew and Y. Wang, “From cognitive psychology to cognitive informatics,” in Proc. 2nd IEEE Int. Conf. Cognitive Informatics, August, 2003, pp. 114–120.
  • D. E. Berger, K. Pezdek and W.P. Banks, Applications of Cognitive Psychology: Problem Solving, Education, and Computing. Hillsdale, NJ: Routledge, 2013.
  • R. A. Bianchetti, “Looking back to inform the future: The role of cognition in forest disturbance characterization from remote sensing imagery,” Ph.D. dissertation, The Pennsylvania State Univ., State College, PA, 2014.
  • J. E. Burge, “Knowledge elicitation for design task sequencing knowledge,” Ph.D. dissertation, Worcester Polytechnic Institute, Worcester, MA, 1998.
  • M. Rejaur Rahman and S. K. Saha. (2008). “Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data,” J. Indian Soc. Remote Sens. 36 (2), pp. 189–201.
  • P. Shao, G. Yang, X. Niu, X. Zhang, F. Zhan and T. Tang. (2014). “Information extraction of high-resolution remotely sensed image based on multiresolution segmentation,” Sustainability 6 (8), pp. 5300–5310.
  • Z. Jin, Y. Pu, J. Ma and G. Chen, “The geographical weighted K-NN classifiers in land cover classification from remote sensing image: A case study of a subregion of Xi'an, China,” in 19th Int. Conf. Geoinformatics, 2011, June, 2011, pp. 1–5.
  • M. N. Murty and V. S. Devi, “Nearest Neighbour based classifiers,” in Pattern Recognition. London: Springer-Verlag, 2011, pp. 48–85.
  • S. Aksoy, I. Z. Yalniz and K. Tasdemir. (2012). “Automatic detection and segmentation of orchards using very high resolution imagery,” IEEE Trans. Geosci. Remote Sens. 50 (8), pp. 3117–3131.
  • S. Aksoy, I. Z. Yalniz and K. Tasdemir. (2012). Automatic detection and segmentation of orchards using very high resolution imagery. IEEE Trans. Geosci. Remote Sens. 50 (8), pp. 3117–3131.
  • A. Manno-Kovacs and A. O. Ok. (2015). “Building detection from monocular VHR images by integrated urban area knowledge,” IEEE Geosci. Remote Sens. Lett. 12 (10), pp. 2140–2144.
  • C. Benedek, X. Descombes and J. Zerubia, “Building detection in a single remotely sensed image with a point process of rectangles,” in 20th Int. Conf. Pattern Recognition, 2010, Aug., 2010, pp. 1417–1420.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.