Journal
IEEE ACCESS
Volume 11, Issue -, Pages 123236-123241Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3323701
Keywords
High-resolution; human visual perception; perception experiences; feature selection
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In this study, a cross-resolution perceptual experiences transfer framework is proposed for categorizing high-resolution aerial photos. By leveraging the perceptual features from low-resolution aerial photos to enhance the feature selection of high-resolution ones, the technique for categorizing a rich variety of high-resolution aerial photos is achieved.
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.
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