How to Quickly Find the Object of Interest in Large Scale Remote Sensing Images
Zhina Song, Haigang Sui, Hua Li · 2018
Detecting geospatial targets in cluttered scenes is a profound challenge in the field of aerial and satellite image, especially some time-sensitive-targets like airplanes, ships, and cars. Most of time the problem firstly we face is how to rapidly judge whether a particular target is included in a large random remote sensing image, instead of detecting them on a given small image. In this paper, we introduced a hierarchical architecture with a coarse to fine strategy to quickly locate the targets. At the coarse layer, we used an improved saliency detection model utilizes multiple salience detection methods to quickly locate suspected regions in a large and complicated remote sensing image. Then at the fine layer with each region, without region proposal method, a single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation is adopted to search small airplane objects. Unlike sliding window and region proposal-based techniques, this method is faster and more robust to target scale variation. Experimental results show the proposed method is quickly identify small targets in large-scale images.