Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery

Cooke, Tristrom; Redding, N.J.; Schroeder, Jim; Zhang, Jingxin

doi:10.1006/dspr.2000.0379

Cited by 8 publications

(9 citation statements)

References 6 publications

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“…Given a test input (ID = 5, bus), the IR-CNN provided a correct answer, while the SAR-CNN failed to recognize it because the SAR signatures of the bus (ID = 5) and oil tanker (ID = 11) were similar. The entropies of SAR and IR were estimated to be 2.7685 and 2.6877, respectively, from the SAR and IR probability distributions using Equations (7) and (8). The corresponding online weights of the SAR and IR sensor were calculated using Equations (5) and (6).…”

Section: Proposed Double Weight-based Sar-ir Fusion Methodsmentioning

confidence: 99%

“…Among several sensors available, infrared (IR) cameras, particularly mid-wave infrared band (3-5 µm), are used frequently in military applications because of the day and night operation capability [1,2]. The research scope of this paper focuses only on ground target recognition assuming that the target regions or locations are detected by IR only [3][4][5], synthetic aperture radar (SAR) only [6][7][8], and fused sensor [9][10][11]. Many military applications prefer to use IR-based target recognition because IR sensors have a passive nature and high image resolution.…”

Section: Introductionmentioning

confidence: 99%

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Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning

2018

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This paper presents a novel double weight-based synthetic aperture radar (SAR) and infrared (IR) sensor fusion method (DW-SIF) for automatic ground target recognition (ATR). IR-based ATR can provide accurate recognition because of its high image resolution but it is affected by the weather conditions. On the other hand, SAR-based ATR shows a low recognition rate due to the noisy low resolution but can provide consistent performance regardless of the weather conditions. The fusion of an active sensor (SAR) and a passive sensor (IR) can lead to upgraded performance. This paper proposes a doubly weighted neural network fusion scheme at the decision level. The first weight (α) can measure the offline sensor confidence per target category based on the classification rate for an evaluation set. The second weight (β) can measure the online sensor reliability based on the score distribution for a test target image. The LeNet architecture-based deep convolution network (14 layers) is used as an individual classifier. Doubly weighted sensor scores are fused by two types of fusion schemes, such as the sum-based linear fusion scheme (αβ-sum) and neural network-based nonlinear fusion scheme (αβ-NN). The experimental results confirmed the proposed linear fusion method (αβ-sum) to have the best performance among the linear fusion schemes available (SAR-CNN, IR-CNN, α-sum, β-sum, αβ-sum, and Bayesian fusion). In addition, the proposed nonlinear fusion method (αβ-NN) showed superior target recognition performance to linear fusion on the OKTAL-SE-based synthetic database.Remote Sens. 2018, 10, 72 2 of 24 by considering both feature extractors and classifiers to cope with IR variations. The Markov tree feature [13], IR wavelet feature [14], scale invariant feature transform (SIFT) [15], histogram of oriented gradients (HOG) [16], and moment features [17,18] are recently proposed infrared features that show promising recognition results on their own applications. Simple machine learning-based classification methods, such as the nearest neighbor classifier [15], Bayesian classifier, conventional neural network, Adaboost [19], and support vector machine (SVM) [16] are used frequently to discriminate the target features for classification.SAR can measure the electromagnetic scattering property of targets under any weather and light conditions [20]. This method is used frequently to recognize a range of targets because it provides a strong radar cross section (RCS) and shape information of non-stealth targets. On the other hand, it produces many false recognitions due to speckle noise [21]. Various SAR features, such as polarimetry and transformations (log-polar, Fourier, and wavelet), are used to discriminate SAR targets [20,[22][23][24]. The standard deviation, fractal dimension, and weighted-rank fill ratio are the basic SAR features proposed in the Lincoln laboratory [22]. A genetic algorithm-based SAR feature selection method has been used to select the optimal features for target recognition [25]. Targets can be recognized by clas...

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Section: Proposed Double Weight-based Sar-ir Fusion Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning

2018

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“…Of course numerous other features may be extracted from an ROI, but here we will highlight only some of the many possibilities for illustration. Multiscale models can be used to capture the variation of the statistical properties of a given region in an image as the resolution is varied [21].…”

Section: Literature Survey Of Target Classification Recognition Andmentioning

confidence: 99%

Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance

Suvorova¹,

Schroeder²

2002

Digital Signal Processing

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“…The second problem is also known as automatic target detection (ATD) problem. Major techniques for ATD include adaptive boosting [3], extended fractal feature [4], genetic programming [5], multiscale autoregressive (MAR), multiscale autoregressive moving average (MARMA) models, singular value decomposition (SVD) methods [6] and constant false alarm rate (CFAR) processing [7]. CFAR processing is widely used to give a globally applicable threshold for a constant probability of false alarms through estimating and removing the local background statistics.…”

Section: Introductionmentioning

confidence: 99%

A target detection scheme for VHF SAR ground surveillance

Paulson

et al. 2008

SPIE Proceedings

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Detection of targets concealed in foliage is a challenging problem and is critical for ground surveillance. To detect foliage-concealed targets, we need to address two major challenges, namely, 1) how to remotely acquire information that contains important features of foliage-concealed targets, and 2) how to distinguish targets from background and clutter. Synthetic aperture radar operated in low VHF-band has shown very good penetration capability in the forest environment, and hence the first problem can be satisfactorily addressed. The second problem is the focus of this paper. Existing detection schemes can achieve good detection performance but at the cost of high false alarm rate. To address the limitation of the existing schemes, in this paper, we develop a target detection algorithm based on a supervised learning technique that maximizes the margin between two classes, i.e., the target class and the non-target class. Specifically, our target detection algorithm consists of 1) image differencing, 2) maximum-margin classifier, and 3) diversity combining. The image differencing is to enhance and highlight the targets so that the targets are more distinguishable from the background. The maximum-margin classifier is based on a recently developed feature weighting technique called I-RELIEF; the objective of the maximum-margin classifier is to achieve robustness against uncertainties and clutter. The diversity combining utilizes multiple images to further improve the performance of detection, and hence it is a type of multi-pass change detection. We evaluate the performance of our proposed detection algorithm, using the SAR image data collected by Swedish CARABAS-II systems which operates at low VHF-band around 20-90 MHz. The experimental results demonstrate superior performance of our algorithm, compared to the benchmark algorithm associated with the CARABAS-II SAR image data. For example, for the same level of target detection probability, our algorithm only produces 11 false alarms while the benchmark algorithm produces 86 false alarms.

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Comparison of Selected Features for Target Detection in Synthetic Aperture Radar Imagery

Cited by 8 publications

References 6 publications

Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning

Double Weight-Based SAR and Infrared Sensor Fusion for Automatic Ground Target Recognition with Deep Learning

Automated Target Recognition Using the Karhunen–Loéve Transform with Invariance

A target detection scheme for VHF SAR ground surveillance

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