Scipediacontent: Scipediacontent moved page Draft Content 354034168 to Etten 2018a

2021-02-03T18:30:23Z

Scipediacontent moved page Draft Content 354034168 to Etten 2018a

Scipediacontent: Created page with " == Abstract == Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels an..."

2021-02-03T18:30:20Z

Created page with " == Abstract == Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels an..."

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== Abstract ==

Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels and geographical extent per image: a single DigitalGlobe satellite image encompasses over 64 km2 and over 250 million pixels. Another challenge is that objects of interest are often minuscule (~pixels in extent even for the highest resolution imagery), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (SIMRDWN) that evaluates satellite images of arbitrarily large size at native resolution at a rate of > 0.2 km2/s. Building upon the tensorflow object detection API paper, this pipeline offers a unified approach to multiple object detection frameworks that can run inference on images of arbitrary size. The SIMRDWN pipeline includes a modified version of YOLO (known as YOLT), along with the models of the tensorflow object detection API: SSD, Faster R-CNN, and R-FCN. The proposed approach allows comparison of the performance of these four frameworks, and can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. For objects of very different scales (e.g. airplanes versus airports) we find that using two different detectors at different scales is very effective with negligible runtime cost.We evaluate large test images at native resolution and find mAP scores of 0.2 to 0.8 for vehicle localization, with the YOLT architecture achieving both the highest mAP and fastest inference speed.

Comment: 8 pages, 7 figures, 2 tables, 1 appendix. arXiv admin note: substantial text overlap with arXiv:1805.09512

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1809.09978 http://arxiv.org/abs/1809.09978]

* [http://arxiv.org/pdf/1809.09978 http://arxiv.org/pdf/1809.09978]

* [http://dx.doi.org/10.1109/wacv.2019.00083 http://dx.doi.org/10.1109/wacv.2019.00083]

* [http://xplorestaging.ieee.org/ielx7/8642793/8658235/08659155.pdf?arnumber=8659155 http://xplorestaging.ieee.org/ielx7/8642793/8658235/08659155.pdf?arnumber=8659155],
: [http://dx.doi.org/10.1109/wacv.2019.00083 http://dx.doi.org/10.1109/wacv.2019.00083]

* [https://arxiv.org/pdf/1809.09978.pdf https://arxiv.org/pdf/1809.09978.pdf],
: [https://dblp.uni-trier.de/db/journals/corr/corr1809.html#abs-1809-09978 https://dblp.uni-trier.de/db/journals/corr/corr1809.html#abs-1809-09978],
: [https://arxiv.org/abs/1809.09978 https://arxiv.org/abs/1809.09978],
: [https://export.arxiv.org/pdf/1809.09978 https://export.arxiv.org/pdf/1809.09978],
: [http://export.arxiv.org/abs/1809.09978 http://export.arxiv.org/abs/1809.09978],
: [https://academic.microsoft.com/#/detail/2893811450 https://academic.microsoft.com/#/detail/2893811450]

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Scipediacontent: Scipediacontent moved page Draft Content 354034168 to Etten 2018a

Scipediacontent: Created page with " == Abstract == Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels an..."