Kilometer level background data acquisition and camouflage feature extraction method

DOI:	10.11809/bqzbgcxb2023.01.022
Keywords:	background acquisition; camouflage features; systematic sampling; cluster analysis; main color extraction
Abstract:	Background acquisition and processing is the first step of camouflage design. In order to improve the accuracy and integrity of kilometer background range sampling, this paper proposes a two stage background sampling method based on aerial imaging by using the system sampling theory. The first level sampling obtains the distribution law of background features and landforms by dividing the background area grid. The second level sampling obtains background detail images according to the “Accept Reject” method. A background feature extraction model is constructed based on the difference measurement of color, distance and texture features. Then, the super pixel image is segmented by simple linear iterative clustering (SLIC) algorithm to form the background feature region division. In the experiment, the data of 12 primary sampling points in a range of 300 km2 are collected, and the proportion and distribution characteristics of background features are analyzed. According to the analysis results, the second level sampling collects three kinds of background data： Gobi desert, farmland villages, and roads. The segmentation performance of farmland village background images under different segmentation parameters is compared, and the differences between k means and super pixel segmentation models are discussed. Finally, three types of background spots and seven main color features are generated. The results show that the sampling process can effectively obtain the kilometer level background data distribution and detailed texture color features. The super pixel segmentation model can optimize the integrity of spot regions. This study provides a basis for the camouflage design of moving targets.
Published:	2023-01-28
PDF HTML