A framework to derive geospatial attributes for aircraft type recognition in large-scale remote sensing images
Rajeshreddy Datla, Vishnu Chalavadi, Krishna Mohan Chalavadi
Abstract
Aircraft type recognition remains challenging, due to their tiny sizes and geometric distortions in large-scale panchromatic satellite images. This paper proposes a framework for aircraft type recognition by focusing on shape preservation, spatial transformations, and geospatial attributes derivation. First, we construct an aircraft segmentation model to obtain masks representing the shape of aircrafts by employing a learnable shape-preserved and deformable network in the mask RCNN architecture. Then, the orientation of the segmented aircrafts is determined by estimating the symmetrical axes using their gradient information. Besides template matching, we derive the length and width of aircrafts using the geotagged information of images to further categorize the types of aircrafts. Also, we present an effective inferencing mechanism to overcome the issue of partial detection or missing aircrafts in large-scale images. The efficacy of the proposed framework is demonstrated on large-scale panchromatic images with ground sampling distances of 0.65m (C2S).