Aerial Scene Understanding Using Deep Wavelet Scattering Network and Conditional Random Field

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9913)

Abstract

This paper presents a fast and robust architecture for scene understanding for aerial images recorded from an Unmanned Aerial Vehicle. The architecture uses Deep Wavelet Scattering Network to extract Translation and Rotation Invariant features that are then used by a Conditional Random Field to perform scene segmentation. Experiments are conducted using the proposed framework on two annotated datasets of 1277 images and 300 aerial images, introduced in the paper. An overall pixel accuracy of 81 % and 78 % is achieved for the datasets. A comparison with another similar framework is also presented.

Keywords

Aerial scene understanding Unmanned aerial vehicle Deep wavelet scattering Conditional random field 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of ECENational Institute of TechnologyWarangalIndia
  2. 2.Department of EngineeringUniversity of CambridgeCambridgeUK
  3. 3.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia

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