Target detection for very high-frequency synthetic aperture radar ground surveillance

Ye, W.; Paulson, Christopher; Wu, Dapeng

doi:10.1049/iet-cvi.2010.0028

Cited by 10 publications

(15 citation statements)

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“…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). The SAR sensor shows higher entropy than the IR sensor because the probability distribution of the SAR sensor is flatter (equally probable) than that of the IR sensor.…”

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

confidence: 99%

“…The SAR sensor shows higher entropy than the IR sensor because the probability distribution of the SAR sensor is flatter (equally probable) than that of the IR sensor. The online weights (β SAR , β IR ) were finally estimated using Equations (5) and (6). The online weight of the IR sensor (β IR = 0.51) was higher than that of the SAR sensor (β SAR = 0.49), which could correct the SAR-CNN information.…”

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: Proposed Double Weight-based Sar-ir Fusion Methodsmentioning

confidence: 99%

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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“…Local signal distribution and phase-based SAR target detection methods have been proposed to overcome the limitations of intensity-based methods [ 30 , 31 ]. Recently, the max-margin based target detection method was proposed [ 32 ]. This method uses I-RELIEF feature weighting and change detection to make the detector robust to noise and unexpected clutter.…”

Section: Introductionmentioning

confidence: 99%

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

Kim

Song

Kim

2016

Sensors

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Long-range ground targets are difficult to detect in a noisy cluttered environment using either synthetic aperture radar (SAR) images or infrared (IR) images. SAR-based detectors can provide a high detection rate with a high false alarm rate to background scatter noise. IR-based approaches can detect hot targets but are affected strongly by the weather conditions. This paper proposes a novel target detection method by decision-level SAR and IR fusion using an Adaboost-based machine learning scheme to achieve a high detection rate and low false alarm rate. The proposed method consists of individual detection, registration, and fusion architecture. This paper presents a single framework of a SAR and IR target detection method using modified Boolean map visual theory (modBMVT) and feature-selection based fusion. Previous methods applied different algorithms to detect SAR and IR targets because of the different physical image characteristics. One method that is optimized for IR target detection produces unsuccessful results in SAR target detection. This study examined the image characteristics and proposed a unified SAR and IR target detection method by inserting a median local average filter (MLAF, pre-filter) and an asymmetric morphological closing filter (AMCF, post-filter) into the BMVT. The original BMVT was optimized to detect small infrared targets. The proposed modBMVT can remove the thermal and scatter noise by the MLAF and detect extended targets by attaching the AMCF after the BMVT. Heterogeneous SAR and IR images were registered automatically using the proposed RANdom SAmple Region Consensus (RANSARC)-based homography optimization after a brute-force correspondence search using the detected target centers and regions. The final targets were detected by feature-selection based sensor fusion using Adaboost. The proposed method showed good SAR and IR target detection performance through feature selection-based decision fusion on a synthetic database generated by OKTAL-SE.

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“…So far, there are two aspects of image retrieval in the image search process. One is the correlation between the retrieval results and target image [20][21], and the other one is the diversity of the results compared to the target image [22][23]. The correlation of the retrieval results is whether the images shown in front of the results list are related to the user's retrieval intention.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of a Combined Art Image Query Method

Mao¹,

Wenlei²,

Wang³

2016

IJDTA

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) Initial query. The tag query method and the semantic query method will be used. The initial query results includes the results searched by both the methods. (2) Reorganization of the initial query results. The repeated images will be cleared and only one image will be left. (3) Distance based results rechecked. The most relative image will be ranked in front of the list and the less correlation image will be cleared. (4) Reordering the images.All the images will be reordered by the distance which reflects the correlation of the query image. According to the experimental verification, the combined method can improve the query precision.

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Target detection for very high-frequency synthetic aperture radar ground surveillance

Cited by 10 publications

References 9 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

Robust Ground Target Detection by SAR and IR Sensor Fusion Using Adaboost-Based Feature Selection

Research and Application of a Combined Art Image Query Method

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