Adaptive Component Discrimination Network for Airplane Detection in Remote Sensing Images

Abstract

Airplane detection and recognition in the high-resolution remote sensing images (RSIs) remain a challenging task due to the factors of multiple view angles, multiple scales, multiple orientations, etc. This article proposes an adaptive component discrimination network for airplane detection and recognition in RSIs, which focuses on various scales from global to local, making full use of the overall contour as well as the dominant component features of airplanes. First, a standardization processing module is proposed for image projection conversion and resolution uniform to alleviate the confusion of different types of airplanes in different resolution images. Second, the rotatable boundingbox-based pyramid network is utilized to extract candidate airplane coordinates and categories. Furthermore, an adaptive aircraft component discrimination method is established for confusing few-shot airplane targets recognition, which consists of a target orientation adaptive adjustment module (OAAM) and a component discrimination module (CDM). OAAM obtains airplanes with the same orientations by predicting the orientation of the slices and rotating them adaptively. All the uniformed slices are then fed into the CDM for dominant components detection, which corrects the target preclassification results, improving the classification performance. Experiments conducted on the 2020 Gaofen Challenge demonstrate the efficacy and superiority of the proposed method.

Keywords

• Airplane detection and recognition
• component discrimination
• target orientation adaptive adjustment