A benchmark data set for aircraft type recognition from remote sensing images

Abstract

Aircraft type recognition from remote sensing images has many civil and military applications. In images obtained with modern technologies such as high spatial resolution remote sensing, even details of aircraft can become visible. With this, the identification of aircraft types from remote sensing images becomes possible. However, the existing methods for this purpose have mostly been evaluated on different data sets and under different experimental settings. This makes it hard to compare their results and judge the progress in the field. Moreover, the data sets used are often not publicly available, which brings difficulties to reproduce the works for fair comparison. This severely limits the progress of research and the state of the art is not entirely clear. To address this problem, we introduce a new benchmark data set for aircraft type recognition from remote sensing images. This data set is called Multi-Type Aircraft Remote Sensing Images (MTARSI), which contains 9’385 images of 20 aircraft types, with complex backgrounds, different spatial resolutions, and complicated variations in pose, spatial location, illumination, and time period. The publicly available MTARSI data set allows researchers to develop more accurate and robust methods for both remote sensing image processing and interpretation analysis of remote sensing object. We also provide a performance analysis of state-of-the-art aircraft type recognition and deep learning approaches on MTARSI, which serves as baseline result on this benchmark.