Spectral Similarity Based Multiscale Spatial-Spectral Preprocessing Framework for Hyperspectral Image Classification
Bibliographic record
Abstract
Hyperspectral imaging represents an advanced technology that offers an extensive array of spectral data concerning various materials.Each pixel within a hyperspectral image encompasses reflectance or transmittance values spanning a spectrum of wavelengths, thereby constructing a spectral signature or spectral curve.Despite the high spectral resolution inherent in hyperspectral images, their spatial resolution frequently remains limited, resulting in a mixture of spectral information within the spectral signatures.This situation presents a significant obstacle to achieving precise hyperspectral image classification, given that both spectral and spatial information play pivotal roles in this endeavor.In the present investigation, a novel spectral-spatial preprocessing strategy is introduced, employing a multiscale filtering technique based on spectral similarity to enhance the accuracy of hyperspectral image classification.The methodology entails performing a neighborhood operation for each target pixel vector, predicated on their spectral resemblance.This operation assigns higher priority to more similar pixels within the neighborhood window to establish the new spectral curve of the pixel of interest.The resultant spectral curves effectively amalgamate both spatial and spectral information and are subsequently utilized during the classification process instead of the original spectral curves.The study incorporates established spectral similarity metrics alongside an innovative metric grounded in Fré chet distance to calculate spectral similarities.The outcomes derived from these metrics are juxtaposed to assess their efficacy in ameliorating the accuracy of hyperspectral image classification.Moreover, the classification performance is evaluated utilizing kernel extreme learning machine and support vector classifiers across four distinct hyperspectral image datasets.The findings underscore that, particularly when confronted with constraints related to small sample sizes, the proposed spectral-spatial preprocessing technique markedly enhances the classification accuracy of hyperspectral images.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".