Saliency detection and LSD linear detection-based airport extraction method

Abstract

The invention relates to a saliency detection and LSD linear detection-based airport extraction method comprising the following steps: an original airport detection data set is built; remote sensing images are extracted one by one from the data set and are subjected to downsampling operation, Frequency-tuned method based saliency detection is conducted on the downsampled images, a full resolution saliency map is formed, linear segments in the saliency map are obtained via LSD linear segment detection operators, the obtained linear segments are segmented and then connected to form long linear segments, parallel straight lines are screened out, obtained parallel straight lines are clustered, a minimum bounding rectangle zone for each type is extracted and then expanded outward by 1.1 times to form a final airport zone, and coordinates of the airport zone are output and drawn up in original remote sensing images. Via LSD linear detection in the airport extraction method disclosed in the invention, texture and edge information in the images can be successfully extracted, FT saliency detection is combined with the LSD linear detection, detection is expedited, and precision is ensured while detection accuracy is enhanced.

Description

technical field [0001] The invention belongs to the field of remote sensing application and computer vision, in particular to an airport extraction method based on saliency detection and LSD line detection. Background technique [0002] Airport detection is one of the important applications in the field of remote sensing and computer vision today. Through the rapid processing of high-resolution and high-temporal remote sensing images, the required information can be obtained quickly, efficiently and economically in the military and civilian fields, which greatly improves the Production efficiency and enhanced national defense capabilities. For airport targets in remote sensing images, it is still very difficult to accurately and quickly detect them. Therefore, it is an important research topic to use computer technology to extract airport areas more efficiently and robustly. So far, researchers at home and abroad have proposed a variety of solutions to the airport detection...

AI Technical Summary

Problems solved by technology

However, the deep learning method requires a large number of manually labeled samples and a large amount of hardware resource support. Each new model requires a lot of time to learn, and is often prone to overfitting and underfitting.

Images