Parallax Visualization of UAV FMV and WAMI Imagery

被引:1
|
作者
Mayhew, Christopher A. [1 ]
Mayhew, Craig M. [1 ]
机构
[1] Vis III Imaging Inc, Vienna, VA USA
关键词
ISR PED; UAV; WAMI; WAAS; FMV; DCGS; parallax visualization; autostereoscopic; stereoscopic; 3D; three-dimensional; square-wave switching; alternating pairs; critical alignment; parallax-scanning;
D O I
10.1117/12.919275
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The US Military is increasingly relying on the use of unmanned aerial vehicles (UAV) for intelligence, surveillance, and reconnaissance (ISR) missions. Complex arrays of Full-Motion Video (FMV), Wide-Area Motion Imaging (WAMI) and Wide Area Airborne Surveillance (WAAS) technologies are being deployed on UAV platforms for ISR applications. Nevertheless, these systems are only as effective as the Image Analyst's (IA) ability to extract relevant information from the data. A variety of tools assist in the analysis of imagery captured with UAV sensors. However, until now, none has been developed to extract and visualize parallax three-dimensional information.(1) Parallax Visualization (PV) is a technique that produces a near-three-dimensional visual response to standard UAV imagery. The overlapping nature of UAV imagery lends itself to parallax visualization. Parallax differences can be obtained by selecting frames that differ in time and, therefore, points of view of the area of interest. PV is accomplished using software tools to critically align a common point in two views while alternately displaying both views in a square-wave manner. Humans produce an autostereoscopic response to critically aligned parallax information presented alternately on a standard unaided display at frequencies between 3 and 6 Hz.(2) This simple technique allows for the exploitation of spatial and temporal differences in image sequences to enhance depth, size, and spatial relationships of objects in areas of interest. PV of UAV imagery has been successfully performed in several US Military exercises over the last two years.(3)
引用
收藏
页数:7
相关论文
共 50 条
  • [1] Parallax Visualization Plug-in Toolset for Pursuer WAMI Data
    Mayhew, Christopher A.
    Mayhew, Craig M.
    Forgues, Mark B.
    [J]. AIRBORNE INTELLIGENCE, SURVEILLANCE, RECONNAISSANCE (ISR) SYSTEMS AND APPLICATIONS X, 2013, 8713
  • [2] DENSE POINT CLOUD EXTRACTION FROM UAV IMAGERY USING PARALLAX ATTENTION
    Bergado, J. R.
    Nex, F.
    [J]. GEOSPATIAL WEEK 2023, VOL. 10-1, 2023, : 1027 - 1032
  • [3] Vehicle Classification in WAMI Imagery using Deep Network
    Yi, Meng
    Yang, Fan
    Blasch, Erik
    Sheaff, Carolyn
    Liu, Kui
    Chen, Genshe
    Ling, Haibin
    [J]. SENSORS AND SYSTEMS FOR SPACE APPLICATIONS IX, 2016, 9838
  • [4] Elimination of Resampling Errors in Wide Area Motion Imagery (WAMI)
    Cohenour, Curtis
    Rovito, Todd
    van Graas, Frank
    [J]. IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE, 2017, 32 (06) : 24 - 32
  • [5] Summary of Methods in Wide-Area Motion Imagery (WAMI)
    Blasch, Erik
    Seetharaman, Guna
    Suddarth, Steve
    Palaniappan, Kannappan
    Chen, Genshe
    Ling, Haibin
    Basharat, Arlsan
    [J]. GEOSPATIAL INFOFUSION AND VIDEO ANALYTICS IV; AND MOTION IMAGERY FOR ISR AND SITUATIONAL AWARENESS II, 2014, 9089
  • [6] Standards for Efficient Employment of Wide Area Motion Imagery (WAMI) Sensors
    Randall, L. Scott
    Maenner, Paul F.
    [J]. MOTION IMAGERY TECHNOLOGIES, BEST PRACTICES, AND WORKFLOWS FOR INTELLIGENCE, SURVEILLANCE, AND RECONNAISSANCE (ISR), AND SITUATIONAL AWARENESS, 2013, 8740
  • [7] MODEL FOR OPTIMAL PARALLAX IN STEREO RADAR IMAGERY
    PISARUCK, MA
    KAUPP, VH
    MACDONALD, HC
    WAITE, WP
    [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 1984, 22 (06): : 564 - 569
  • [8] Calibration of Satellite Imagery with Multispectral UAV Imagery
    Kamal Jain
    Akshay Pandey
    [J]. Journal of the Indian Society of Remote Sensing, 2021, 49 : 479 - 490
  • [9] Calibration of Satellite Imagery with Multispectral UAV Imagery
    Jain, Kamal
    Pandey, Akshay
    [J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2021, 49 (03) : 479 - 490
  • [10] Vehicle Pose Estimation in WAMI Imagery via Deep Convolutional Neural Networks
    Yi, Meng
    Wang, Dong
    Yang, Fan
    Xu, Jonathan
    Cai, Yiran
    Blasch, Erik
    Sheaff, Carolyn
    Chen, Genshe
    Ling, Haibin
    [J]. PROCEEDINGS OF THE 2016 IEEE NATIONAL AEROSPACE AND ELECTRONICS CONFERENCE (NAECON) AND OHIO INNOVATION SUMMIT (OIS), 2016, : 233 - 240