Closely spaced object detection utilizing spatial information in spectroastrometric observations

Gazak, J. Zachary; Swindle, Thomas R.; Funke, Zachary; Phelps, Matthew; Fletcher, Justin

doi:10.1117/12.2625366

Cited by 4 publications

(4 citation statements)

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“…In this field, researchers often use their own datasets, and these datasets have different qualities and different imaging conditions; at the same time, there is a lack of benchmarks to uniformly compare the performances of all methods. Therefore, we applied several representative methods of different types as comparative methods, including the 3-D CNN [12][13][14]23,24], the CNMF [9], and the TD [7], wherein the proposed method and the 3-D CNN are fully supervised, the CNMF is semi-supervised, and the TD is unsupervised.…”

Section: Experimental Datamentioning

confidence: 99%

“…Then, as the convolutional neural network (CNN) showed advantages in feature extraction, researchers turned their attention to the CNN [12] and proposed a variety of CNN-based identification methods. Deng et al [13] and Gazak et al [14] adopted a one-dimensional (1-D) CNN to deal with 1-D measured data, in which only spectral features played a role.…”

Section: Introductionmentioning

confidence: 99%

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Space Target Material Identification Based on Graph Convolutional Neural Network

Gong

Zhao

et al. 2023

Remote Sensing

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Under complex illumination conditions, the spectral data distributions of a given material appear inconsistent in the hyperspectral images of the space target, making it difficult to achieve accurate material identification using only spectral features and local spatial features. Aiming at this problem, a material identification method based on an improved graph convolutional neural network is proposed. Superpixel segmentation is conducted on the hyperspectral images to build the multiscale joint topological graph of the space target global structure. Based on this, topological graphs containing the global spatial features and spectral features of each pixel are generated, and the pixel neighborhoods containing the local spatial features and spectral features are collected to form material identification datasets that include both of these. Then, the graph convolutional neural network (GCN) and the three-dimensional convolutional neural network (3-D CNN) are combined into one model using strategies of addition, element-wise multiplication, or concatenation, and the model is trained by the datasets to fuse and learn the three features. For the simulated data and the measured data, the overall accuracy of the proposed method can be kept at 85–90%, and their kappa coefficients remain around 0.8. This proves that the proposed method can improve the material identification performance under complex illumination conditions with high accuracy and strong robustness.

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Section: Experimental Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Space Target Material Identification Based on Graph Convolutional Neural Network

Gong

Zhao

et al. 2023

Remote Sensing

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“…These include but are not limited to: sensor and telescope degradation (data drift), space weathering / damage to satellite materials (class drift), seasonal atmospheric effects (covariate drift), and new class appearance. 23 We leave these for future investigations using on sky data.…”

Section: Concept Driftmentioning

confidence: 99%

Model validity dynamics in learned spectroscopic recognition of satellites

Gazak

Phelps

Swindle

et al. 2023

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX

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Recent work demonstrates that convolutional neural networks can be trained to recognize artificial satellites from spatially unresolved ground-based observations (SpectraNet). SpectraNet enables space domain awareness (SDA) catalogs to be enriched with object identity, a critical source of information for space domain stakeholders. As learned spectral SDA matures, conditions for training and deploying performant and calibrated neural network recognition algorithms must be measured. In this work we present a simulated three year baseline of observations using a longslit spectrograph on a single telescope. We use this dataset to develop a framework for measuring baseline data requirements for performant SpectraNet models, and for testing the performance of those models after deployment. On this limited (single telescope, longslit spectrograph) setup, the presented framework returns a performant model after three weeks of collections. Further, we find that a model can be deployed for a full annual cycle after twenty six weeks of data collection, and the model reaches maximum sustained inference performance after a year. Thus a SpectraNet powered longslit spectrograph can provide tactical inferences after a few weeks and be retrained to infer through seasonal variability during deployment. We find that the simulated system and dataset regularly exceed 82% classification accuracy, and discuss performance improvements with enhanced instrumentation and/or multi-telescope networks.

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“…In the SDA enterprise, convolutional neural networks have been applied to object detection, detection of closely spaced objects, pose estimation, reconstruction of high resolution imagery, and segmentation of satellites. [2][3][4][5][6] In these examples, solutions learned from high contrast scientific imagery solve problems faster and more effectively than physics based methods.…”

Section: Learned Space Domain Awarenessmentioning

confidence: 99%

The potential for satellite recognition from ground-based filter photometry

Gazak,

Phelps,

Swindle

et al. 2023

Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX

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Recent work demonstrates recognition of artificial satellites in spatially unresolved observations by utilizing learned spectroscopic classification (SpectraNet 1 ). That proof of concept exposes critical identifying information currently lacking in catalogs used by space domain awareness stakeholders. In this work we present experiments to increase the accessibility and efficiency of SpectraNet enabled systems by probing the bandpass and resolution requirements for learned recognition of satellites. To enable affordable, off the shelf instrumentation, this work focuses on wavelength ranges accessible by Silicon-based detectors (400-1000 nanometers). While the SpectraNet proof of concept utilized a medium resolution spectrograph on a 3.6 meter telescope at 10,000 feet elevation, we show that the identifying spectral features relate to an object's overall spectral energy density and are accessible at significantly lower spectral resolution. This finding relaxes the need for large telescopes at high altitude. We further demonstrate that the technology can be utilized via simultaneous multi-band filter photometry. Design considerations for properly obtaining simultaneous photometry are discussed. Thus this work demonstrates that−in simulation−learned spectral recognition is an effective technology from high resolution spectrographs through simultaneous multi-filter photometric instruments. We provide experiments to understand the minimum engineered system needed to perform effective learned recognition, such that the technology can be hardened and widely proliferated.

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Closely spaced object detection utilizing spatial information in spectroastrometric observations

Cited by 4 publications

References 0 publications

Space Target Material Identification Based on Graph Convolutional Neural Network

Space Target Material Identification Based on Graph Convolutional Neural Network

Model validity dynamics in learned spectroscopic recognition of satellites

The potential for satellite recognition from ground-based filter photometry

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