High-Resolution Aerial Photo Categorization Model by Cross-Resolution Perceptual Experiences Transfer

There are thousands of observation satellites orbiting the earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photos are with high-resolution and usually contain tens to hundreds of ground objects (e.g., vehicles and roo...

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Bibliographic Details
Published in:IEEE access 2023, Vol.11, p.123236-123241
Main Authors: Li, Sida, Liu, Ye
Format: Article
Language:English
Subjects:
Algorithms
Comparative studies
Computational modeling
Feature extraction
Feature selection
High resolution
High-resolution imaging
human visual perception
Kernel
Natural disasters
Perception evolution networks
perception experiences
Satellite observation
Satellites
Semantics
Spatial resolution
Visual perception
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Summary:There are thousands of observation satellites orbiting the earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photos are with high-resolution and usually contain tens to hundreds of ground objects (e.g., vehicles and rooftops). Understanding the categories of a rich variety of high-resolution aerial photos is an indispensable technique for many applications, such as intelligent transportation, natural disaster prediction, and smart agriculture. In this work, we propose a cross-resolution perceptual experiences transfer framework for categorizing high-resolution aerial photos, focusing on leveraging the perceptual features from low-resolution aerial photos to enhance the feature selection of high-resolution ones. More specifically, we first construct gaze shifting path to mimic human visual perception to both low-resolution and high-resolution aerial photos, wherein the corresponding deep gaze shifting path features are engineered. Afterward, a kernel-induced feature selection algorithm is formulated to obtain a succinct set of deep gaze shifting path features discriminative across low- and high-resolution aerial photos. Based on the selected features, low- and high-resolution aerial photos' labels are collaboratively utilized to train a linear classifier for categorizing high-resolution ones. Extensive comparative studies have validated the superiority of our method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3323701