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7 May 2019 Ground object detection in worldview images
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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.
Conference Presentation
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Chiman Kwan, Bryan Chou, David Gribben, Leif Hagen, Jerry Yang, Bulent Ayhan, and Krzysztof Koperski "Ground object detection in worldview images", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 1101817 (7 May 2019); https://doi.org/10.1117/12.2518529
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