Robust and adaptive band-to-band image transform of UAS miniature multi-lens multispectral camera

Jyun Ping Jhan, Jiann Yeou Rau, Norbert Haala

研究成果: Article

6 引文 (Scopus)

摘要

Utilizing miniature multispectral (MS) or hyperspectral (HS) cameras by mounting them on an Unmanned Aerial System (UAS) has the benefits of convenience and flexibility to collect remote sensing imagery for precision agriculture, vegetation monitoring, and environment investigation applications. Most miniature MS cameras adopt a multi-lens structure to record discrete MS bands of visible and invisible information. The differences in lens distortion, mounting positions, and viewing angles among lenses mean that the acquired original MS images have significant band misregistration errors. We have developed a Robust and Adaptive Band-to-Band Image Transform (RABBIT) method for dealing with the band co-registration of various types of miniature multi-lens multispectral cameras (Mini-MSCs) to obtain band co-registered MS imagery for remote sensing applications. The RABBIT utilizes modified projective transformation (MPT) to transfer the multiple image geometry of a multi-lens imaging system to one sensor geometry, and combines this with a robust and adaptive correction (RAC) procedure to correct several systematic errors and to obtain sub-pixel accuracy. This study applies three state-of-the-art Mini-MSCs to evaluate the RABBIT method's performance, specifically the Tetracam Miniature Multiple Camera Array (MiniMCA), Micasense RedEdge, and Parrot Sequoia. Six MS datasets acquired at different target distances and dates, and locations are also applied to prove its reliability and applicability. Results prove that RABBIT is feasible for different types of Mini-MSCs with accurate, robust, and rapid image processing efficiency.

原文English
頁(從 - 到)47-60
頁數14
期刊ISPRS Journal of Photogrammetry and Remote Sensing
137
DOIs
出版狀態Published - 2018 三月

指紋

Lenses
transform
Cameras
cameras
lenses
Antennas
Mathematical transformations
imagery
Mountings
remote sensing
precision agriculture
geometry
Remote sensing
multispectral image
image processing
mounting
Geometry
pixel
Systematic errors
sensor

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences

引用此文

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