Authors:
Rasna Amit
and
C. Mohan
Affiliation:
Indian Institute of Technology Hyderabad, Kandi, Sangareddy, Telangana, 502285, India
Keyword(s):
Dynamic Kernel, Gaussian Mixture Model, MAP Adaptation, Object Representations, Remote Sensing Images, Scale Effect.
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 representation
s 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.
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