This article [J. Appl. Remote Sens.. 8, (1 ), 083601 ( July 25 2014)] contained an error in the abstract that changed the meaning of a sentence. The original sentence read, “It is shown that magnitude and power detections, respectively, degrade the spatial resolution by factors of two and greater.” The corrected sentence reads, “It is shown that power and magnitude detections, respectively, degrade the spatial resolution by factors of two and greater.”
The complex-valued image output from a synthetic aperture radar (SAR) processor possesses full spatial resolution defined by the sensor. Typically, this image is either power detected or magnitude detected before it is subjected to further analysis. This paper consists of the study of the effect of detection on spatial resolution in complex-valued SAR imagery and the mitigation of this effect through upsampling. Furthermore, an algorithm for upsampling focused SAR imagery is presented. The proposed algorithm is general and it is designed to account for deviations from zero-Doppler encountered in the Spotlight imaging mode. It is shown that power and magnitude detections, respectively, degrade the spatial resolution by factors of two and greater. To mitigate this effect, the complex-valued SAR image should be upsampled appropriately. The results are demonstrated on real-world single-look complex SAR imagery from Radarsat-2.
This paper presents a novel algorithm for upsmapling level-1 processed (i.e., focused) Spotlight SAR imagery. A
Spotlight Radarsat-2 single look complex (SLC) image for ground-truthed vehicle targets in Long-Harbour,
Newfoundland (Canada) is used to demonstrate the applicability of our proposed algorithm. To achieve a finer resolution
in the azimuth direction, the Spotlight imaging mode allows for a controllable steering of the radar antenna towards the
same ground position. In effect, this creates a time-varying Doppler centroid system, wherein the Doppler centroid varies
almost linearly with the platform velocity. Although the focused Spotlight Radarsat-2 SLC imagery is delivered
referenced to zero-Doppler, linear variations in the Doppler frequency are preserved along the range direction around the
zero-Doppler line. The impact of this effect on SAR image upsampling is pinpointed and accounted for in our proposed
Target detection is the front-end stage in any automatic target recognition system for synthetic aperture radar (SAR) imagery (SAR-ATR). The efficacy of the detector directly impacts the succeeding stages in the SAR-ATR processing chain. There are numerous methods reported in the literature for implementing the detector. We offer an umbrella under which the various research activities in the field are broadly probed and taxonomized. First, a taxonomy for the various detection methods is proposed. Second, the underlying assumptions for different implementation strategies are overviewed. Third, a tabular comparison between careful selections of representative examples is introduced. Finally, a novel discussion is presented, wherein the issues covered include suitability of SAR data models, understanding the multiplicative SAR data models, and two unique perspectives on constant false alarm rate (CFAR) detection: signal processing and pattern recognition. From a signal processing perspective, CFAR is shown to be a finite impulse response band-pass filter. From a statistical pattern recognition perspective, CFAR is shown to be a suboptimal one-class classifier: a Euclidian distance classifier and a quadratic discriminant with a missing term for one-parameter and two-parameter CFAR, respectively. We make a contribution toward enabling an objective design and implementation for target detection in SAR imagery.