Superresolution for UAV Images via Adaptive Multiple Sparse Representation and Its Application to 3-D Reconstruction

Muhammad Haris, Takuya Watanabe, Liu Fan, Muhammad Rahmat Widyanto, Hajime Nobuhara

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

Abstract

We propose a superresolution (SR) algorithm based on adaptive sparse representation via multiple dictionaries for images taken by unmanned aerial vehicles (UAVs). The SR attainable through the proposed algorithm can increase the precision of 3-D reconstruction from UAV images, enabling the production of high-resolution images for constructing high-frequency time series and for high-precision digital mapping in agriculture. The basic idea of the proposed method is to use a field server or ground-based camera to take training images and then construct multiple pairs of dictionaries based on selective sparse representations to reduce instability during the sparse coding process. The dictionaries are classified on the basis of the edge orientation into five clusters: 0, 45, 90, 135, and nondirection. The proposed method is expected to reduce blurring, blocking, and ringing artifacts especially in edge areas. We evaluated the proposed and previous methods using peak signal-to-noise ratio, structural similarity, feature similarity, and computation time. Our experimental results indicate that the proposed method clearly outperforms other state-of-the-art algorithms based on qualitative and quantitative analysis. In the end, we demonstrate the effectiveness of our proposed method to increase the precision of 3-D reconstruction from UAV images.

Original languageEnglish
Article number7900406
Pages (from-to)4047-4058
Number of pages12
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume55
Issue number7
DOIs
Publication statusPublished - 1 Jul 2017

Keywords

  • 3-D images
  • aerial image
  • agriculture
  • monitoring
  • phenotyping
  • sparse representation
  • superresolution (SR)
  • unmanned aerial vehicle (UAV)

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