Abstract
This paper proposes a new automatic and adaptive aircraft target detection algorithm in high-resolution synthetic aperture radar (SAR) images of airport. The proposed method is based on gradient textural saliency map under the contextual cues of apron area. Firstly, the candidate regions with the possible existence of airport are detected from the apron area. Secondly, directional local gradient distribution detector is used to obtain a gradient textural saliency map in the favor of the candidate regions. In addition, the final targets will be detected by segmenting the saliency map using CFAR-type algorithm. The real high-resolution airborne SAR image data is used to verify the proposed algorithm. The results demonstrate that this algorithm can detect aircraft targets quickly and accurately, and decrease the false alarm rate.
Highlights
The ability to provide high resolution images of synthetic aperture radar (SAR) in any weather and at any time is extremely important for disaster monitoring, modern military reconnaissance and combat tasks
We propose Gradient Textural Saliency (GTS) map to represent the likelihood of a pixel being target by using directional local gradient distribution
The detectors for comparison are: Variability Index CFAR (VI-CFAR) [13], SCCA-CFAR [13], two level CFAR detector (TL-CFAR) detectors [15], local edge distributions (LED) detector [23], and Gradient Textual Saliency Map over apron and candidate regions (GTS detector)
Summary
The ability to provide high resolution images of synthetic aperture radar (SAR) in any weather and at any time is extremely important for disaster monitoring, modern military reconnaissance and combat tasks. It is very crucial to elaborately devise an algorithm to detect targets accurately and robustly from SAR images for a specific application scenario even though a unified framework is strongly desired. It is still an open issue to propose the target detection algorithm by considering the SAR image features and application contextual cues. Target detection in a SAR image involves namely extracting the targets of interest from a scene with a background which usually is contaminated by heavy noise. This problem has been studied by many researchers until now. Based on the analysis of [4], the methods of target detection from SAR images can be broadly classified into three types: single-feature based, multi-feature based, and expert-system-oriented
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