Airport surveillance activities using remote sensing images are challenging due to object variations largely affecting the geo-localization and object detection/segmentation tasks. Furthermore, the problem of localization is even larger due to scale variations. Traditionally image-based geo-referencing is accomplished by superimposing ground positioning system (GPS) location to the queried image. It is also observed both the query and the geo-tagged reference images are taken from the same ground view or aerial height in the case of remote sensing images. In our research, we intend to revisit the scale effect on object variability, by introducing the concept of geodesic representations along with image-matching networks. The architecture pipeline introduces a data processing layer wherein objects are geo-referenced to generate the metadata information. This metadata consists of three-dimensional data including the orientation information of the object. A regression task is added to the training set which leverages the metadata information. We use the gradient weighted class activation maps (Grad-CAM) to generate the activation maps and selection based on high threshold values for the pixel. The orientations and the locations are further calculated using the geodesic representations. The baseline architecture for local feature extraction uses a simple Siamese network with a ResNet backbone network. A NetVLAD layer is used to generate the global features. We also introduce a Geospatial attention network (GsAN) to aid in enhanced localization of objects. The dataset used for experiments consisted of CVUSA and our custom dataset providing airport runway views for different scales and arbitrary orientations. The performance evaluations focused on recall as a retrieval metric and comparing various loss functions. The performance metrics indicate a higher accuracy rate.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Geodesic Based Image Matching Network for the Multi-scale Ground to Aerial Geo-localization


    Contributors:


    Publication date :

    2023-03-04


    Size :

    33062445 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Geodesic Matching with Free Extremities

    Garcin, L. / Younes, L. | British Library Online Contents | 2006


    Aerial Map-Based Navigation by Ground Object Pattern Matching

    Youngjoo Kim / Seungho Back / Dongchan Song et al. | DOAJ | 2024

    Free access

    Vehicle Ego-Localization by Matching In-Vehicle Camera Images to an Aerial Image

    Noda, M. / Takahashi, T. / Deguchi, D. et al. | British Library Conference Proceedings | 2011


    Multi-image matching using multi-scale oriented patches

    Brown, M. / Szeliski, R. / Winder, S. | IEEE | 2005


    A Reparameterisation Based Approach to Geodesic Constrained Solvers for Curve Matching

    Cotter, C. J. / Clark, A. / Peiró, J. | British Library Online Contents | 2012