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Ground object detection in worldview images
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Paper Abstract

Ground object detection is important for many civilian applications. Counting the number of cars in parking lots can provide very useful information to shop owners. Tent detection and counting can help humanitarian agencies to assess and plan logistics to help refugees. In this paper, we present some preliminary results on ground object detection using high resolution Worldview images. Our approach is a simple and semi-automated approach. A user first needs to manually select some object signatures from a given image and builds a signature library. Then we use spectral angle mapper (SAM) to automatically search for objects. Finally, all the objects are counted for statistical data collection. We have applied our approach to tent detection for a refugee camp near the Syrian-Jordan border. Both multispectral Worldview images with eight bands at 2 m resolution and pansharpened images with four bands at 0.5 m resolution were used. Moreover, synthetic hyperspectral (HS) images derived from the above multispectral (MS) images were also used for object detection. Receiver operating characteristics (ROC) curves as well as detection maps were used in all of our studies.

Paper Details

Date Published: 7 May 2019
PDF: 9 pages
Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 1101817 (7 May 2019); doi: 10.1117/12.2518529
Show Author Affiliations
Chiman Kwan, Applied Research, LLC (United States)
Bryan Chou, Applied Research, LLC (United States)
David Gribben, Applied Research, LLC (United States)
Leif Hagen, Applied Research, LLC (United States)
Jerry Yang, Applied Research, LLC (United States)
Bulent Ayhan, Applied Research, LLC (United States)
Krzysztof Koperski, Digital Globe, Inc. (United States)


Published in SPIE Proceedings Vol. 11018:
Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII
Ivan Kadar; Erik P. Blasch; Lynne L. Grewe, Editor(s)

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