Quantitative Analysis to Find the Optimum Scale Range for Object Representations in Remote Sensing Images

Rasna Amit, C. Mohan

2023

Abstract

Airport object surveillance using big data requires high temporal frequency remote sensing observations. However, the spatial heterogeneity and multi-scale, multi-resolution properties of images for airport surveillance tasks have led to severe data discrepancies. Consequently, artificial intelligence and deep learning algorithms suffer from accurate detections and effective scaling of remote sensing information. The quantification of intra-pixel differences may be enhanced by employing non-linear estimating algorithms to reduce its impact. An alternate strategy is to define scales that help minimize spatial and intra-pixel variability for various image processing tasks. This paper aims to demonstrate the effect of scale and resolution on object representations for airport surveillance using remote sensing images. In our method, we introduce dynamic kernel-based representations that aid in adapting the spatial variability and identify the optimum scale range for object representations for seamless airport surveillance. Airport images are captured at different spatial resolutions and feature representations are learned using large Gaussian Mixture Models (GMM). The object classification is done using a support vector machine and the optimum range is identified. Dynamic kernel GMMs can handle the disparities due to scale variations and image capturing by effectively preserving the local structure information, similarities, and changes in spatial contents globally for the same context. Our experiments indicate that the classification performance is better when both the first and second-order statistics for the Gaussian Mixture Models are used.

Paper Citation

in Harvard Style

Amit R. and Mohan C. (2023). Quantitative Analysis to Find the Optimum Scale Range for Object Representations in Remote Sensing Images. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP; ISBN 978-989-758-634-7, SciTePress, pages 369-379. DOI: 10.5220/0011599200003417

in Bibtex Style

@conference{visapp23,
author={Rasna Amit and C. Mohan},
title={Quantitative Analysis to Find the Optimum Scale Range for Object Representations in Remote Sensing Images},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP},
year={2023},
pages={369-379},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011599200003417},
isbn={978-989-758-634-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 5: VISAPP
TI - Quantitative Analysis to Find the Optimum Scale Range for Object Representations in Remote Sensing Images
SN - 978-989-758-634-7
AU - Amit R.
AU - Mohan C.
PY - 2023
SP - 369
EP - 379
DO - 10.5220/0011599200003417
PB - SciTePress