Advertisement

Extracting Buildings from Satellite Images Using Feature Extraction Methods

  • Jeberson Retna RajEmail author
  • Senduru Srinivasulu
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 3)

Abstract

Extracting buildings in a satellite image is of paramount important for urban planning, detecting changes of landscape and analysis. Extracting building in the satellite image is complex as its micro features are difficult to infer on the image. Feature extraction techniques readily available to extract the built up areas. In this paper, the five feature extraction techniques which include edge detection, grey scale morphological operation, haralick texture extraction, morphological classification and local statistics methods are introduced and compared. The satellite image is registered and geometric correction has performed for further processing. The image is applied with filtering algorithm to remove noises and then these five feature extraction algorithms are applied with the image. The results are more promising and the buildings are extracted from the test images.

Keywords

Satellite images Texture extraction Segmentation Edge extraction 

References

  1. 1.
    Konstantinidis D, Stathaki T (2017) Building detection using enhanced HOG–LBP features and region refinement processes. IEEE J Sel Top Appl Earth Obs Remote Sens 10(3):888–905CrossRefGoogle Scholar
  2. 2.
    Huang X (2017) A new building extraction postprocessing framework for high-spatial-resolution remote-sensing imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 10(2):654–668CrossRefGoogle Scholar
  3. 3.
    Manandhar P, Aung Z, Marpu PR (2017) Segmentation based building detection in high resolution satellite images. IEEE international geoscience and remote sensing symposium (IGARSS), pp 3783–3786Google Scholar
  4. 4.
    Partovi T, Bahmanyar R, Krauß T, Reinartz P (2017) Building outline extraction using a heuristic approach based on generalization of line segments. IEEE J Sel Top Appl Earth Obs Remote Sens 10(3):933–947CrossRefGoogle Scholar
  5. 5.
    Du S, Zhang Y, Zou Z, Xu S, He X, Chen S (2017) Automatic building extraction from LiDAR data fusion of point and grid-based features. ISPRS J Photogrammetry Remote Sens 130:294–307CrossRefGoogle Scholar
  6. 6.
    Xia S, Wang R (2018) Extraction of residential building instances in suburban areas from mobile LiDAR data. ISPRS J Photogrammetry Remote Sens 144:453–468CrossRefGoogle Scholar
  7. 7.
    Alshehhi R, Marpu PR, Woon WL, Dalla Mura M (2017) Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS J Photogrammetry Remote Sens 130:139–149CrossRefGoogle Scholar
  8. 8.
    Turker M, Koc-San D (2015) Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. Int J Appl Earth Obs Geoinf 34:58–69CrossRefGoogle Scholar
  9. 9.
    Haralick RM, Shanmugam K et al (1973) Textural features for image classification. IEEE Trans Systems, Man, Cybern 6:610–621CrossRefGoogle Scholar
  10. 10.
    Pesaresi M, Benediktsson JA (2001) A new approach for the morphological segmentation of high resolution satellite imagery. IEEE Trans Geosci Remote Sens 39(2):309–320CrossRefGoogle Scholar
  11. 11.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Information TechnologySathyabama Institute of Science and TechnologyChennaiIndia

Personalised recommendations