  We propose a new method to count objects of specific categories that are
significantly smaller than the ground sampling distance of a satellite image.
This task is hard due to the cluttered nature of scenes where different object
categories occur. Target objects can be partially occluded, vary in appearance
within the same class and look alike to different categories. Since traditional
object detection is infeasible due to the small size of objects with respect to
the pixel size, we cast object counting as a density estimation problem. To
distinguish objects of different classes, our approach combines density
estimation with semantic segmentation in an end-to-end learnable convolutional
neural network (CNN). Experiments show that deep semantic density estimation
can robustly count objects of various classes in cluttered scenes. Experiments
also suggest that we need specific CNN architectures in remote sensing instead
of blindly applying existing ones from computer vision.
