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Diffractive Neural Network Enabled Spectral Object Detection.

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Title: Diffractive Neural Network Enabled Spectral Object Detection.
Authors: Ma, Yijun1,2,3 (AUTHOR), Chen, Rui1,2,3 (AUTHOR), Qian, Shuaicun1,2,3 (AUTHOR), Sun, Shengli1,3 (AUTHOR) palm_sun@mail.sitp.ac.cn
Source: Remote Sensing. Oct2025, Vol. 17 Issue 19, p3381. 19p.
Subjects: Remote sensing, Optical computing, Object recognition (Computer vision), Infrared detectors, Parallel programming, Spectrum analysis, Infrared equipment
Abstract: Highlights: What are the main findings? We proposed an innovative DNN-SOD diffractive neural network architecture that leverages spectral characteristics and field-of-view segmentation to enable direct spectral feature reconstruction and target detection for infrared targets. The architecture achieved 84.27% on an infrared target dataset, demonstrating its feasibility for large-scale remote sensing tasks. What is the implication of the main finding? This study presents a new paradigm of applying optical computing to spectral remote sensing target detection, overcoming the limitations of traditional optical computing methods that fail to fully exploit spectral properties of targets and handle large-scale data effectively. It provides a novel pathway for integrated sensing-computing information processing in future sky-based remote sensing, highlighting the potential of optical computing inference in real-world applications. This article introduces a diffractive neural network-enabled spectral object detection approach (DNN-SOD) to efficiently process massive sky-based multidimensional light field data. DNN-SOD combines the novel exploitation of target spectral features with the intrinsic parallelism of optical computing to process multidimensional information efficiently. DNN-SOD detects targets by segmenting the spectral data cube and processing it with the DNN. The DNN maps spectral intensity to the designated area of the detector, then reconstructs spectral curves, and differentiates targets by comparing them with reference spectral signatures. Classification results from individual sub-spectral data cubes are compiled in sequence, enabling accurate target detection. Simulation results indicate that the architecture achieved an accuracy of 91.56% on the MNIST multi-spectral dataset and 84.27% on the infrared target multi-spectral dataset, validating its feasibility for target detection. This architecture represents an innovative outcome at the intersection of remote sensing and optical computing, significantly advancing the dissemination and practical adoption of optical computing in the field. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Diffractive Neural Network Enabled Spectral Object Detection.
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  Data: Highlights: What are the main findings? We proposed an innovative DNN-SOD diffractive neural network architecture that leverages spectral characteristics and field-of-view segmentation to enable direct spectral feature reconstruction and target detection for infrared targets. The architecture achieved 84.27% on an infrared target dataset, demonstrating its feasibility for large-scale remote sensing tasks. What is the implication of the main finding? This study presents a new paradigm of applying optical computing to spectral remote sensing target detection, overcoming the limitations of traditional optical computing methods that fail to fully exploit spectral properties of targets and handle large-scale data effectively. It provides a novel pathway for integrated sensing-computing information processing in future sky-based remote sensing, highlighting the potential of optical computing inference in real-world applications. This article introduces a diffractive neural network-enabled spectral object detection approach (DNN-SOD) to efficiently process massive sky-based multidimensional light field data. DNN-SOD combines the novel exploitation of target spectral features with the intrinsic parallelism of optical computing to process multidimensional information efficiently. DNN-SOD detects targets by segmenting the spectral data cube and processing it with the DNN. The DNN maps spectral intensity to the designated area of the detector, then reconstructs spectral curves, and differentiates targets by comparing them with reference spectral signatures. Classification results from individual sub-spectral data cubes are compiled in sequence, enabling accurate target detection. Simulation results indicate that the architecture achieved an accuracy of 91.56% on the MNIST multi-spectral dataset and 84.27% on the infrared target multi-spectral dataset, validating its feasibility for target detection. This architecture represents an innovative outcome at the intersection of remote sensing and optical computing, significantly advancing the dissemination and practical adoption of optical computing in the field. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17193381
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        Text: English
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        PageCount: 19
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      – SubjectFull: Optical computing
        Type: general
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Infrared detectors
        Type: general
      – SubjectFull: Parallel programming
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      – SubjectFull: Spectrum analysis
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      – SubjectFull: Infrared equipment
        Type: general
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      – TitleFull: Diffractive Neural Network Enabled Spectral Object Detection.
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            NameFull: Ma, Yijun
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            NameFull: Chen, Rui
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            NameFull: Sun, Shengli
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            – D: 01
              M: 10
              Text: Oct2025
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              Y: 2025
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