An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images

Gao, Gui; Liu, Li; Zhao, Lingjun; Shi, Gongtao; Kuang, Gangyao

doi:10.1109/tgrs.2008.2006504

Cited by 273 publications

(54 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The adaptive window concept has been applied to targets detection in many applications in the last decade, such as vehicles detection [36,37], adaptive filters [38], and anomaly detection [39,40]. When compared to SW CEM, which uses a fixed window size to calculate autocorrelation matrices S, AWS CEM determines the window size according to the spatial and spectral characteristics around each pixel, so as to suppress the background.…”

Section: Adaptive Sliding Window-based Cem (Asw Cem)mentioning

confidence: 99%

Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

et al. 2018

View full text Add to dashboard Cite

Abstract:Leaf maturation from initiation to senescence is a phenological event of plants that results from the influences of temperature and water availability on physiological activities during a life cycle. Detection of newly grown leaves (NGL) is therefore useful for the diagnosis of tree growth, tree stress, and even climatic change. This paper applies Constrained Energy Minimization (CEM), which is a hyperspectral target detection technique to spot grown leaves in a UAV multispectral image. According to the proportion of NGL in different regions, this paper proposes three innovative CEM based detectors: Subset CEM, Sliding Window-based CEM (SW CEM), and Adaptive Sliding Window-based CEM (AWS CEM). AWS CEM can especially adjust the window size according to the proportion of NGL around the current pixel. The results show that AWS CEM improves the accuracy of NGL detection and also reduces the false alarm rate. In addition, the results of the supervised target detection depend on the appropriate signature. In this case, we propose the Optimal Signature Generation Process (OSGP) to extract the optimal signature. The experimental results illustrate that OSGP can effectively improve the stability and the detection rate.

show abstract

Section: Adaptive Sliding Window-based Cem (Asw Cem)mentioning

confidence: 99%

Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Another relevant algorithm is reported in Gao et al 62 The algorithm builds on the beta-prime CFAR (β'-CFAR) algorithm 31 reported earlier in this paper. A binary index map is created based on globally thresholding the input SAR image.…”

Section: Expert-system-oriented Taxonmentioning

confidence: 99%

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

El-Darymli

McGuire

Power

et al. 2013

J. Appl. Remote Sens

184

112

View full text Add to dashboard Cite

Abstract. 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 Euclidean 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. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

show abstract

“…Consequently, the FAR drops, but the PD degrades, known as the capture effect . Numerous studies [16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44] have been carried out by means of extracting the clutter pixels in the background window and eliminating the influence of the high-intensity outliers. The order statistic CFAR (OS-CFAR) [16] is designed to overcome the above problem arisen in CA-CFAR.…”

Section: Introductionmentioning

confidence: 99%

A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery

Yang

Zhou

et al. 2017

Sensors

View full text Add to dashboard Cite

Traditional constant false alarm rate (CFAR) detectors only use the contrast information between ship targets and clutter, and they suffer probability of detection (PD) degradation in multiple target situations. This paper proposes a correlation-based joint CFAR detector using adaptively-truncated statistics (hereafter called TS-2DLNCFAR) in SAR images. The proposed joint CFAR detector exploits the gray intensity correlation characteristics by building a two-dimensional (2D) joint log-normal model as the joint distribution (JPDF) of the clutter, so joint CFAR detection is realized. Inspired by the CFAR detection methodology, we design an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers, such as interfering ship targets, side-lobes, and ghosts in the background window, whereas the real clutter samples are preserved to the largest degree. A 2D joint log-normal model is accurately built using the adaptively-truncated clutter through simple parameter estimation, so the joint CFAR detection performance is greatly improved. Compared with traditional CFAR detectors, the proposed TS-2DLNCFAR detector achieves a high PD and a low false alarm rate (FAR) in multiple target situations. The superiority of the proposed TS-2DLNCFAR detector is validated on the multi-look Envisat-ASAR and TerraSAR-X data.

show abstract

An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images

Cited by 273 publications

References 19 publications

Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

Adaptive Window-Based Constrained Energy Minimization for Detection of Newly Grown Tree Leaves

Target detection in synthetic aperture radar imagery: a state-of-the-art survey

A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery

Contact Info

Product

Resources

About