C
Christine E. Blinn
Researcher at Virginia Tech
Publications - 22
Citations - 472
Christine E. Blinn is an academic researcher from Virginia Tech. The author has contributed to research in topics: Forest inventory & Median filter. The author has an hindex of 10, co-authored 22 publications receiving 378 citations. Previous affiliations of Christine E. Blinn include Rochester Institute of Technology.
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On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data
TL;DR: A method that utilizes residuals from harmonic regression over years of Landsat data, in conjunction with statistical quality control charts, to signal subtle disturbances in vegetative cover, which is able to detect changes from both deforestation and subtler forest degradation and thinning.
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Approximating Prediction Uncertainty for Random Forest Regression Models
TL;DR: A Monte Carlo approach to quantify prediction uncertainty for random forest regression models by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors.
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The impact of improved signal-to-noise ratios on algorithm performance: Case studies for Landsat class instruments
John R. Schott,Aaron Gerace,Curtis E. Woodcock,Curtis E. Woodcock,Shixiong Wang,Shixiong Wang,Zhe Zhu,Zhe Zhu,Randolph H. Wynne,Randolph H. Wynne,Christine E. Blinn,Christine E. Blinn +11 more
TL;DR: The Landsat Operational Land Imager (OLI) has 5 to 10 times better signal-to-noise ratios (SNRs) in all spectral bands than previous Landsat instruments and performance was shown to be a strong function of SNR with substantial increase in performance as SNR increased.
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An Adaptive Noise-Filtering Algorithm for AVIRIS Data With Implications for Classification Accuracy
TL;DR: A new algorithm used to adaptively filter a remote-sensing data set based on signal-to-noise ratios (SNRs) once the maximum noise fraction has been applied, which improves image quality and classification accuracies.
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Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier
TL;DR: The results presented here indicate that SVD-based feature reduction can produce statistically significantly better classifications than PCA.