Paper Info
Title
Aircraft Type Recognition in Remote Sensing Images using Mean Interval Kernel
Abstract
Structural characteristics representation and their fine variations are crucial for the recognition of different types of aircrafts in remote sensing images. Aircraft type classification across different sensor remote sensing images by spectral and spatial resolutions of objects in an image involves variable length spatial pattern identification. In our proposed approach, we explore dynamic kernels to deal with variable length spatial patterns of aircrafts in remote sensing images. A Gaussian mixture model (GMM), namely, structure model (SM) is trained over aircraft scenes to implicitly learn the local structures using the spatial scale-invariant feature transform (SIFT) features. The statistics of SM are used to design dynamic kernel, namely, mean interval kernel (MIK) to deal with the spatial changes globally in the identical scene and preserve the similarities in local spatial structures. The efficacy of the proposed method is demonstrated on the multi-type aircraft remote sensing images (MTARSI) benchmark dataset (20 distinct kinds of aircraft) using MIK. Also, we compare the performance of the proposed approach with other dynamic kernels, such as supervector kernel (SVK) and intermediate matching kernel (IMK).
Year
DOI
Venue
2022
10.5220/0011062600003209
IMPROVE: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND VISION ENGINEERING
Keywords
DocType
Citations 
Remote Sensing Images, Aircraft Type Recognition, Structural Information Model, Scale-invariant Feature Transform (SIFT), Dynamic Kernels
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Jaya Sharma100.34
Rajeshreddy Datla200.34
Yenduri Sravani300.34
Vishnu Chalavadi412.06
Krishna C. Mohan500.34