Detection of Camouflage-Covered Military Objects Using High-Resolution Multi-Spectral Satellite Imagery

Cannaday, Alan B.; Davis, Curt H.; Bajkowski, Trevor M.

doi:10.1109/igarss52108.2023.10281409

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium 2023

DOI: 10.1109/igarss52108.2023.10281409

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Detection of Camouflage-Covered Military Objects Using High-Resolution Multi-Spectral Satellite Imagery

Alan B. Cannaday,

Curt H. Davis,

Trevor M. Bajkowski

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2024

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Cited by 2 publications

References 17 publications

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Hyperspectral Image Classification Method Based on Morphological Features and Hybrid Convolutional Neural Networks

Ran,

Shi,

Zhang

et al. 2024

Applied Sciences

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The exploitation of the spatial and spectral characteristics of hyperspectral remote sensing images (HRSIs) for the high-precision classification of earth observation targets is crucial. Convolutional neural networks (CNNs) have good classification performance and are widely used neural networks. Herein, a morphological processing (MP)-based HRSI classification method and a 3D–2D CNN are proposed to improve HRSI classification accuracy. Principal component analysis is performed to reduce the dimensionality of the HRSI cube, and MP is implemented to extract the spectral–spatial features of the low-dimensional HRSI cube. The extracted features are concatenated with the low-dimensional HRSI cube, and the designed 3D–2D CNN framework completes the classification task. Residual connections and an attention mechanism are added to the CNN structure to prevent gradient vanishing, and the scale of the control parameters of the model structure is optimized to guarantee the model’s feature extraction ability. The CNN structure uses multiscale convolution, involving depthwise separable convolution, which can effectively reduce the amount of parameter calculation. Two classic datasets (Indian Pines and Pavia University) and a self-made dataset (My Dataset) are used to compare the performance of this method with existing classification techniques. The proposed method effectively improved classification accuracy despite its short classification time.

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Hyperspectral Image Classification Method Based on Morphological Features and Hybrid Convolutional Neural Networks

Ran,

Shi,

Zhang

et al. 2024

Applied Sciences

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Research on the Technology of a Compact Double-Layer Multispectral Filter-Wheel Mechanism Driven by a Single Motor

Ma,

Wu,

Tan

et al. 2024

Applied Sciences

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Spatial multispectral imaging technology can selectively image in specific spectral bands, and the filter wheel is a core component for multispectral selection. At present, there are relatively few types of spectral bands for the filter wheel under limited space/weight constraints. Addressing the challenges presented by this issue, this paper introduces an innovative design approach for the development of a double-layer or even multi-layer filter wheel that is operated by a solitary motor in conjunction with a differential gear mechanism, enabling a vast array of spectral segment combinations within a highly compact layout. A detailed design is implemented for the double-layer filter wheel, including comprehensive modal and dynamic analyses. The results of the modal analysis attested to the structural stability of the component, and the outcomes of the dynamic analysis validated the component’s timely and reliable switching capabilities. A prototype was meticulously crafted and subjected to rigorous testing. The switching functionality was validated during these tests, concurrently affirming the accuracy of the finite element analysis results. Additionally, spectral and application testing confirmed the number of spectral segments and the practical utility of the components. The research presented in this article introduces an innovative design concept for multispectral imaging filter-wheel mechanisms, providing a valuable reference and profound insights for the design and arrangement of a double-layer or even multi-layer filter wheel.

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Detection of Camouflage-Covered Military Objects Using High-Resolution Multi-Spectral Satellite Imagery

Cited by 2 publications

References 17 publications

Hyperspectral Image Classification Method Based on Morphological Features and Hybrid Convolutional Neural Networks

Hyperspectral Image Classification Method Based on Morphological Features and Hybrid Convolutional Neural Networks

Research on the Technology of a Compact Double-Layer Multispectral Filter-Wheel Mechanism Driven by a Single Motor

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