Researchers develop ultrafast wavemeter that employs spectral–spatial–temporal mapping. Read full article here: https://hubs.ly/Q02zPWz90
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This paper presents an improved Gaussian mixture #probability #hypothesis #density (#GM-#PHD) multi-target tracking pipeline. The proposed GM-PHD solution considers (i) radar bounding-box- based measurements, (ii) a clutter model for radar observations, and (iii) robust target identification. Using both simulated and experimental scenarios, these contributions are proven to improve the tracking performance in terms of robustness against noise and clutter, as well as target identification. The significance of the presented #multitarget #tracking (#MTT) scheme lies in its low complexity since it does not need any data association. This makes it applicable for intense high-level applications for which MTT is used. ----S. Hamed Javadi, Ruoyu Feng, André Bourdoux More details can be found at this link: https://lnkd.in/ggnNRCfZ
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Professor and Group Leader | Artificial Intelligence for Earth Observation | PhD in Electrical and Computer Engineering | Helmholtz (HZDR-HIF) & Lancaster University
One Model to Rule Them All! [One model for any tasks in the remote sensing field]! #SpectralGPT, a pioneer foundation model in #remotesensing (RS) and #geosciences led by Danfeng Hong, has been trained on over 1 million spectral images of varying sizes, resolutions, and time series. This model enables comprehensive utilization of extensive RS big data and has been utilized for multiple downstream tasks including #sceneclassification, #semanticsegmentation, and #changedetection. Learn more about it here: https://lnkd.in/d9xccYmQ https://lnkd.in/dcRtC5-A #BigEarthNet #fMoW #foundationmodels #earthobservation
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"Systems of many interacting agents display an increase in diversity, distribution, and/or patterned behavior when numerous configurations of the system are subject to selective pressure." "Configurations of matter tend to persist unless kinetically favorable avenues exist for their incorporation into more stable configurations." https://lnkd.in/grbaHs3K #roles #functions #evolvingsystems
On the roles of function and selection in evolving systems | Proceedings of the National Academy of Sciences
pnas.org
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Remote Sensing | Earth Science | Spatial Data Analytics | Machine Learning | Deep Learning| python |
Do you believe it would be conceivable to employ an automatic mask-generating system rather than doing it manually? Using a "Grounded System," you may extract ground features from any image captured by a remote sensing platform. Link to the repository: https://lnkd.in/gTbcgBF5 I experimented, and I believe that further work is necessary to improve the outcome. Link to the example https://lnkd.in/gxaexDW3
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🎥🌡️ Continue your infrared education with the second video of our series! Join Palmer Wahl President Stephen Santangelo as he tackles the age-old question: 'What is Spectral Range?' Understanding and choosing the correct spectral range for your infrared temperature sensor is crucial for accurate readings. Dive into the world of spectral ranges and discover their importance in precise temperature measurement. Watch the full video here: https://lnkd.in/ggE7ZyZv #InfraredInsights #SpectralRangeExplained #PalmerWahlKnowledgeSeries
What is Spectral Range?
https://www.youtube.com/
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The following spectrum is obtained using Wavelet and Fourier transforms.
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A very helpful list of spectral Indices for remote sensing applications: https://lnkd.in/eNGFRsQi
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enabling digital services for Student Loan related activities while maintaining the highest security standard, the most compliant personal data protection and customer-centric data-driven innovation.
Excited to share a new blog post on a Multisensor Hyperspectral Benchmark Dataset for Unmixing of Intimate Mixtures! Optical hyperspectral cameras capture the complex relationship between spectral reflectance and material composition. To validate spectral unmixing algorithms, high-quality ground truth fractional abundance data are crucial but hard to obtain. In this study, a comprehensive laboratory dataset of intimately mixed mineral powders was generated, providing valuable data for validating advanced methods in material composition estimation. Download the dataset here: https://bit.ly/44J8Zdd #spectralunmixing #hyperspectraldata #materialcomposition
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Unlock the full potential of spectral datal with greater precision. Check out the enhanced target detection, material identification, and other tools in ENVI 6.0. https://bit.ly/4bBv3uH #ENVI #SpectralData #RemoteSensing
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Riemannian Geometric Matrix-CFAR Detector in Radar, invention by Thales https://lnkd.in/ezeBywke "A novel clutter suppression method, the matrix-CFAR, was recently proposed and developed, which does not require any a priori information and leverages the autocovariance matrix of the signals [13], [14]. This method operates under the premise that the autocovariance matrix, which characterizes the autocorrelation of observation data, is Hermitian positive-definite (HPD) and has been leveraged for detection problems [15], [16]. Its detection efficiency has been demonstrated in the observation of wake eddy turbulence [17], Burg estimate methods of radar scatter matrices [18], detection of X-band radar clutter [19], and so on. In these applications, the affine invariant Riemannian metric (AIRM) of the HPD manifolds was used ... As future research, we plan to explore the practical applications of the matrix-CFAR technique on real-world field data. Second, Gaussian distributions for HPD matrices can be established through a symplectic model introduced by [83] and [84], which may be applied in determining the threshold of matrix-CFAR."
The Comparison of Riemannian Geometric Matrix-CFAR Signal Detectors
ieeexplore.ieee.org
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