Object counting, whose aim is to estimate the number of objects from a given
image, is an important and challenging computation task. Significant efforts
have been devoted to addressing this problem and achieved great progress, yet
counting the number of ground objects from remote sensing images is barely
studied. In this paper, we are interested in counting dense objects from remote
sensing images. Compared with object counting in a natural scene, this task is
challenging in the following factors: large scale variation, complex cluttered
background, and orientation arbitrariness. More importantly, the scarcity of
data severely limits the development of research in this field. To address
these issues, we first construct a large-scale object counting dataset with
remote sensing images, which contains four important geographic objects:
buildings, crowded ships in harbors, large-vehicles and small-vehicles in
parking lots. We then benchmark the dataset by designing a novel neural network
that can generate a density map of an input image. The proposed network
consists of three parts namely attention module, scale pyramid module and
deformable convolution module to attack the aforementioned challenging factors.
Extensive experiments are performed on the proposed dataset and one crowd
counting datset, which demonstrate the challenges of the proposed dataset and
the superiority and effectiveness of our method compared with state-of-the-art
methods.

在本文中，我们致力于研究如何从遥感图像中计数密集物体，针对这一难题，我们构建了一个大规模的遥感图像计数数据集，设计了一种新型的神经网络可以生成一个输入图像的密度图，并进行广泛的实验证明了我们方法的卓越性和有效性。