Investigation of nonlinearity in hyperspectral remotely sensed imagery — a nonlinear time series analysis approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hyperspectral remotely sensed imagery is often modeled and processed by algorithms assuming that the imagery is a realization of a Gaussian linear stochastic process. These algorithms include some methods for feature extraction, spectral mixture analysis, and spatial analysis. The linear assumption, however, may not be realistic since there are factors that may introduce nonlinearities during the formulation of hyperspectral imagery. The existence of nonlinearity has a negative impact on the effectiveness and accuracy of information extraction. In this study, we propose a method to investigate the existence of nonlinearity in hyperspectral data, represented by a 4m AVIRIS image acquired over an area of coastal forests on Vancouver Island. The proposed method is based on a statistical test using surrogate data, an approach originally introduced in nonlinear time series analysis. High-order autocorrelations are used as the discriminating statistic to evaluate the differences between the hyperspectral data and their surrogates. Instead of conducting a statistical test in time domain as is used in typical time series analysis, we did it in spatial and spectral domains. The investigation revealed that the existence of nonlinearity in hyperspectral data is evident in spectral domain, but not in the spatial domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it