Infrared Image Complexity Metric for Automatic Target Recognition Based on Neural Network and Traditional Approach Fusion

Wang, Xiaotian; Zhang, Kai; Yan, Jie; Xing, Muzeng; Yang, Dongsheng

doi:10.1007/s13369-020-04351-7

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…The figures display the intuitive effect of all the algorithms. The detection comparison of the Tophat, max-mean filter, max-median filter, MPCM [12], LMLCM [20], WSLCM [21], and our method is shown in Figures 8-13 All the methods were compared for the complex background image sequences (1), (2), and (3) captured in the outfield. The SCRG and BSF of image (1) are shown in Table 4.…”

Section: Simulation Results and Comparisonmentioning

confidence: 99%

“…This article uses a fusion algorithm based on the target’s SCR. SCR stands for the background complexity to some extent [ 21 ]. When the SCR was higher than 4, we selected a local gradient contrast enhancement algorithm based on template filtering.…”

Section: Methodsmentioning

confidence: 99%

“…The false alarm rate is defined as the ratio of the number of false targets detected to the total number of false targets, as is shown in ( 13 ) [ 21 ].

…”

Section: Methodsmentioning

confidence: 99%

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An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing

et al. 2023

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Space vehicles such as missiles and aircraft have relatively long tracking distances. Infrared (IR) detectors are used for small target detection. The target presents point target characteristics, which lack contour, shape, and texture information. The high-brightness cloud edge and high noise have an impact on the detection of small targets because of the complex background of the sky and ground environment. Traditional template-based filtering and local contrast-based methods do not distinguish between different complex background environments, and their strategy is to unify small-target template detection or to use absolute contrast differences; so, it is easy to have a high false alarm rate. It is necessary to study the detection and tracking methods in complex backgrounds and low signal-to-clutter ratios (SCRs). We use the complexity difference as a prior condition for detection in the background of thick clouds and ground highlight buildings. Then, we use the spatial domain filtering and improved local contrast joint algorithm to obtain a significant area. We also provide a new definition of gradient uniformity through the improvement of the local gradient method, which could further enhance the target contrast. It is important to distinguish between small targets, highlighted background edges, and noise. Furthermore, the method can be used for parallel computing. Compared with the traditional space filtering algorithm or local contrast algorithm, the flexible fusion strategy can achieve the rapid detection of small targets with a higher signal-to-clutter ratio gain (SCRG) and background suppression factor (BSF).

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Section: Simulation Results and Comparisonmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing

et al. 2023

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“…The combination of formula (5) and formula (3) can obtain the Hu stock moment with translation, zoom and rotation functions, which can increase the influence factor of radial distortion during the acquisition process, and reduce the deformation caused by the acquisition of SAR images by the hardware device. The problem of reduced recognition accuracy is discussed in [24].…”

Section:  mentioning

confidence: 99%

“…The performance of the algorithm is critical to the range and intelligence of the automatic target recognition system. Recognition of small and dim targets in SAR images is a difficult subject with important strategic application value [5].…”

Section: Introductionmentioning

confidence: 99%

Recognition Method of Dim and Small Targets in SAR Images based on Machine Vision

Dong¹

2022

IJACSA

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Aiming at the problems of long recognition time and low recognition accuracy of traditional SAR image dim target recognition methods, a method of SAR image dim target recognition based on machine vision was proposed. SAR images are collected and preprocessed by machine vision, and the image information is processed by PCA dimension reduction considering the linear characteristics of the data to extract image features. Then, the SAR image target feature key frame frequency band is divided by the segmentation results, and the recognition model is established based on the image trajectory tracking and target analysis. The proposed algorithm is applied and analyzed. The simulation results show that the proposed algorithm has good recognition rate, average recognition rate and false detection rate are 99% and 0.9%, and can effectively ensure the data processing performance.

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NSMT: A Novel Non-subsampled Morphological Transform Fusion Algorithm for Infrared–Visible Images

Hu,

Wang,

et al. 2023

Circuits Syst Signal Process

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Infrared Image Complexity Metric for Automatic Target Recognition Based on Neural Network and Traditional Approach Fusion

Cited by 7 publications

References 20 publications

An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing

An Adaptive Infrared Small-Target-Detection Fusion Algorithm Based on Multiscale Local Gradient Contrast for Remote Sensing

Recognition Method of Dim and Small Targets in SAR Images based on Machine Vision

NSMT: A Novel Non-subsampled Morphological Transform Fusion Algorithm for Infrared–Visible Images

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