Impact of Geometric Priors: Advanced Fine-grained Airplane Detection with Geometric Details in High-resolution Satellite Images
TL;DR
A geometry-aware classification that enhances oriented object detectors by integrating absolute measures and geometric features – fuselage length, wingspan, wing sweep angle, engine count, and fuselage width – in the form of priors into a Bayesian maximum a posteriori (MAP) estimation.
Abstract
Abstract. Improved availability and quality of high-resolution satellite imagery allow for reliable airplane detection. Yet, fine-grained classification, especially of commercial airliners, remains a formidable challenge. Besides common difficulties, such as varying image artifacts and occlusions, the main challenge lies in the strong visual similarity between airliner families. This paper presents a geometry-aware classification that enhances oriented object detectors by integrating absolute measures and geometric features – fuselage length, wingspan, wing sweep angle, engine count, and fuselage width – in the form of priors into a Bayesian maximum a posteriori (MAP) estimation. The proposed pipeline is detector-agnostic by updating class posteriors without retraining the main detector. On the Gaofen Challenge dataset, it results in consistent improvements based on untuned baseline detectors, which out-perform the top scores of the sophisticated fine-tuned models. An oracle experiment reveals the potential of the approach with an upper limit of the overall mean Average Precision of up to 0.96 and 0.98 for Gaofen and SuperView data, respectively. Furthermore, the impact of the employed geometric attributes is quantitatively evaluated.