English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Though it has great significance for military and civilian applications, detecting aircraft from large-scale remote sensing images remains a challenge. This is a typical small target detection problem, which has not been solved well yet. Because a large-scale satellite image usually encompasses &gt;150 km 2  and over 500 million pixels, while the aircrafts are often minuscule (only about 80 pixels). To solve this problem, we propose an aircraft detection method based on visual saliency and convolutional neural networks. First, the improved Fast R-CNN network is used to classify the airport. YOLOv4-Less, an improved YOLOv4 network, judges whether an aircraft is at the airport. In experiments, the network model was trained and tested through an image data set collected from Google Earth. The results showed that the accuracy of the model can reach 98.04% and the recall rate can reach 97.84%. Furthermore, the proposed algorithm detected aircraft in remote sensing images from different satellite platforms, and the experimental results showed that its adaptability is strong, and detection efficiency was improved compared to existing methods.

Aircraft detection from large-scale remote sensing images based on visual saliency and CNNs