  We describe a strategy for detection and classification of man-made objects
in large high-resolution satellite photos under computational resource
constraints. We detect and classify candidate objects by using five pipelines
of convolutional neural network processing (CNN), run in parallel. Each
pipeline has its own unique strategy for fine tunning parameters, proposal
region filtering, and dealing with image scales. The conflicting region
proposals are merged based on region confidence and not just based on overlap
areas, which improves the quality of the final bounding-box regions selected.
We demonstrate this strategy using the recent xView challenge, which is a
complex benchmark with more than 1,100 high-resolution images, spanning 800,000
aerial objects around the world covering a total area of 1,400 square
kilometers at 0.3 meter ground sample distance. To tackle the
resource-constrained problem posed by the xView challenge, where inferences are
restricted to be on CPU with 8GB memory limit, we used lightweight CNN's
trained with the single shot detector algorithm. Our approach was competitive
on sequestered sets; it was ranked third.
