期刊
IEEE ACCESS
卷 11, 期 -, 页码 123236-123241出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2023.3323701
关键词
High-resolution; human visual perception; perception experiences; feature selection
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据