Open AccessAircraft Detection for HR SAR Images in Non‐homogeneous Background Using GGMD‐Based Modeling
Author(s) -
Hu Hao,
Huang Lanqing,
Yu Wenxian
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.08.010
Subject(s) - constant false alarm rate , computer science , homogeneous , synthetic aperture radar , artificial intelligence , false alarm , pattern recognition (psychology) , gamma distribution , radar , computer vision , mathematics , statistics , telecommunications , combinatorics
In the problem of aircraft detection for High resolution (HR) Synthetic aperture radar (SAR) images, the background areas commonly contain multiple land cover types, such as runways and grassland. The conventional Constant false alarm rate (CFAR) detection in these non‐homogeneous backgrounds with homogeneous assumption leads to unreliable detection results. This paper constructs a one‐stage detection method based on the Generalized gamma mixture distribution (GGMD), which is regarded as a competitive and applicable model for combining the advantages of the Generalized gamma distribution (GGD) and the Finite mixture model (FMM). In order to evaluate the availability of the proposed algorithm, HR SAR images for aircraft detection from different product types and with various resolutions are examined. Compared with the CFAR algorithms based on the Gamma distribution, the GGD, and the gamma mixture distribution, the proposed algorithm demonstrates its availability and effectiveness for aircraft detection in HR SAR images in non‐homogeneous background.