One of the common tasks in remote sensing research is object recognition, i.e., detection followed by classification, in satellite images. Object recognition algorithms are widely used in various industrial and military applications. We contribute to the academic research by focusing on airplane recognition in satellite images. In our paper, we utilize existing state-of-the-art deep learning models combined with (1) separating detection and classification and (2) normalizing the input image data concerning rotation in a way that the head of an aircraft always points in the same given direction. This leads to a considerable improvement of both detection and classification accuracy of airplanes as demonstrated by the results from the FAIR1M data set.      «

One of the common tasks in remote sensing research is object recognition, i.e., detection followed by classification, in satellite images. Object recognition algorithms are widely used in various industrial and military applications. We contribute to the academic research by focusing on airplane recognition in satellite images. In our paper, we utilize existing state-of-the-art deep learning models combined with (1) separating detection and classification and (2) normalizing the input image data...     »