Open AccessVisual saliency mechanism‐based object recognition with high‐resolution remote‐sensing images
Author(s) -
He Lin,
Li Chen
Publication year - 2020
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1155
Subject(s) - computer vision , computer science , artificial intelligence , cognitive neuroscience of visual object recognition , object (grammar) , low resolution , high resolution , resolution (logic) , mechanism (biology) , image (mathematics) , remote sensing , pattern recognition (psychology) , geography , philosophy , epistemology
Object recognition with remote‐sensing image is widely used in many areas. Some objects are smaller and denser in the high‐resolution images, such as the oil tank, ship, and aircraft. The recognition of this kind of objects is more difficult than the objects with low‐resolution images, for example, bridges and airports. The recognition performance is more dependent on the shallower features. The contour of these objects is obvious, and the characteristics are quite different from background, which satisfies the human visual saliency mechanism. Here, the authors propose a novel theme of object recognition method based on visual saliency mechanism for remote‐sensing images with sub‐meter resolution. The experimental results show that the proposed method performs best compared with other algorithm.