Authors:
Jaya Sharma
1
;
Rajeshreddy Datla
2
;
1
;
Yenduri Sravani
1
;
Vishnu Chalavadi
1
and
Krishna Mohan C.
1
Affiliations:
1
Department of Computer Science and Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India
;
2
Advanced Data Processing Research Institute (ADRIN), Department of Space, Secunderabad, India
Keyword(s):
Remote Sensing Images, Aircraft Type Recognition, Structural Information Model, Scale-invariant Feature Transform (SIFT), Dynamic Kernels.
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).
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