Evaluation of MTARSI2 Dataset for Aircraft Type Recognition in Remote Sensing Images

Abstract

The potential of implementing Remote sensing in terms of applications has been extended due to the significant enhancement in the spatial and spectral resolution. One of the most significant application in remote sensing image interpretation is Aircraft Type Recognition (ATR), which has been extensively exploited in both civil and military applications. ATR via remote sensing images has attracted the attention of scientists and researchers due to its critical role in aerospace applications and intelligence information. The field of research in this area has been improved as the knowledge is narrowed to evaluate different private datasets due to the unavailability of a benchmark dataset. Thus, the level of obtained certainty of the overall performances achieved in this application area is remarkably jeopardized, therefore, reducing the reproducibility of results significantly. Recently, two benchmark datasets, namely: Multi-Type Aircraft Remote Sensing Images (MTARSI) and its extended version MTARSI2, have been published in 2020 and 2021, respectively. The researchers are still in the exploratory stage to develop methods with high level of accuracy. Therefore, the scope of work of this research is to conduct a detailed analysis on the extended data set MTARSI2 to evaluate the dataset’s characteristics, strength, weakness, and performance against well-known algorithms. Moreover, the study will target the latest limitations of ATR in remote sensing images and recommend possible scenarios and solution to be studied in further detailed future research work.