Dynamic scene simulation techonlogy used for infrared seeker

Zhou, Qiong

doi:10.1117/12.833441

Cited by 4 publications

(4 citation statements)

References 6 publications

(6 reference statements)

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“…The TMM technique [ 26 ] has been used extensively in the HSI simulator (e.g., the CameoSim package) [ 32 , 33 , 34 ], which estimates the abundances by evaluating the inverse of the Euclidean distances of each EM with respect to the spectral characteristics of the mixed pixel, i.e., the test pixel. Note that the results that have been presented in Section 5.2.2 are the reconstructions that have been processed without the limitation on the

.…”

Section: Resultsmentioning

confidence: 99%

“…Two different ways to constraint the N mp have been implemented here: one is the use of the FNNOMP (i.e., the Algorithm 2, KMSCD+FNNOMP in Section 3), and the other employs the Texture Material Mapper (TMM) method (i.e., the KMSCD+TMM). The TMM technique [26] has been used extensively in the HSI simulator (e.g., the CameoSim package) [32][33][34], which estimates the abundances by evaluating the inverse of the Euclidean distances of each EM with respect to the spectral characteristics of the mixed pixel, i.e., the test pixel. Note that the results that have been presented in Section 5.2.2 are the reconstructions that have been processed without the limitation on the N mp .…”

Section: Kmscd For Scene Simulation Applicationsmentioning

confidence: 99%

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Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

Chatterjee

Yuen

2019

J. Imaging

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This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary D u . The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.

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.…”

Section: Resultsmentioning

confidence: 99%

Section: Kmscd For Scene Simulation Applicationsmentioning

confidence: 99%

Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

Chatterjee

Yuen

2019

J. Imaging

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“…By comparing field measurements with Ansys analysis, a temperature field model of the target is established. Through temperature field mapping, the obtained temperature field is mapped onto the Vega scene simulation platform 10 , using its Sensor module to visualize the infrared simulation scene. Additionally, particle effects are utilized to simulate dust and smoke, adding complexity to the simulation images to make them more closely resemble real-world scenarios.…”

Section: Infrared Target Imaging Simulationmentioning

confidence: 99%

Simulation and recognition technology for infrared target imaging

Liu,

Zhang,

Jin

et al. 2024

Speckle 2023: VIII International Conference on Speckle Metrology

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In the field of infrared target recognition for air-to-ground surveillance, challenges such as the difficulty in collecting large-scale infrared empirical data and the reduced accuracy of target recognition in complex environments like dust and smoke have been faced. This study presents a technology scheme for simulating target imaging in the mid-wave and long-wave infrared spectra under varying concentrations of dust and smoke in terrestrial backgrounds. Additionally, an improved YOLOv8 deep learning network for target recognition, incorporating group convolution and an EMA attention mechanism, was designed. Experimental results reveal that the simulated data generated by the developed target imaging simulation technology exhibits a high degree of similarity to real-world data. Furthermore, the improved target recognition deep learning network, after undergoing transfer learning, achieved a precision rate of 82.34% and a recall rate of 90.67%. The developed infrared target imaging simulation and recognition technology provide a new technological approach for the development of deep learning network technology in target recognition for both military and civilian ground-based surveillance applications.

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“…The infrared guided missile detection simulator mainly has the following design schemes: large screen projection, five-axis rotary table, optical mechanical and zoom projection [4][5][6][7]. The large-screen projection-type infrared simulation system [8], an infrared guided semi-physical simulation detection device built by the American Boeing Aerospace Corporation in the 1970s, consists of four parts: an infrared target generation system, a large reflective screen, a three-axis turntable, and a control computer.…”

Section: Introductionmentioning

confidence: 99%

Development of Infrared-Guided Missile Precision Detection Simulator

Wang

et al. 2021

Machines

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In order to carry out various detections of system indicators during the research and development phase of infrared guided missiles, the article first analyzes several main design schemes of the infrared guided missile detection simulator and finds that it has the disadvantages of difficult processing technology and low detection accuracy. The overall structure of the detection device was designed, including the design of the rotation and swing mechanism, lens mechanism, optical system and control system. The optical system error analysis is performed on the infrared guided missile detection simulator. The position of the receiving light source is obtained by analyzing the mechanism characteristics of the detection simulator and the kinematics model of the device. The phase difference analysis of the eccentricity and tilt system is obtained. The image quality was evaluated by the optical transfer function (MTF), and the system error was found to meet the requirements of imaging quality. The experiments show that the simulation of 1.7~4.9 um medium wave infrared dynamic target signals provides an accurate and reasonable experimental environment for the missile and the verification of the light source target and meets the experimental requirements.

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Dynamic scene simulation techonlogy used for infrared seeker

Cited by 4 publications

References 6 publications

Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

Simulation and recognition technology for infrared target imaging

Development of Infrared-Guided Missile Precision Detection Simulator

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