Investigation of nonlinearity in hyperspectral remotely sensed imagery — a nonlinear time series analysis approach
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".