Spectral-Spatial Classification of Vegetative Cover Types Using Hyperspectral Data
Bibliographic record
Abstract
1. Перспективные информационные технологии дистанционного зондирования Земли: Моногр. / Под ред. В. А. Сойфера. Самара: Новая техника, 2015. 256 с. 2. Chen C., Li W., Tramel E.W., Cui M., Prasad S., Fowler J. E. Spectral-spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification // IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2014. Vol. 7. Р. 1047–1059. 3. Palsson F., Ulfarsson M. O., Sveinsson J. R. Hyperspectral image denoising using a sparse low rank model and dual-tree complex wavelet transform // Proc. of the Geoscience and Remote Sensing Symposium (IGARSS), IEEE International. 2014. P. 3670–3673. 4. Zhen Ye, Mingyi He, Fowler J. E., Qian Du. Hyperspectral image classification based on spectra derivative features and locality preserving analysis // Proc. of the Signal and Information Processing (ChinaSIP), IEEE China Summit & International Conference. 2014. P. 138–142. 5. Borhani M., Ghassemian H. Hyperspectral Image Classification Based on Spectral-Spatial Features Using Probabilistic SVM and Locally Weighted Markov Random Fields // Iranian Conference on Intelligent Systems (ICIS 2014). 2014. P. 1–6. 6. Yang Hu, Eli Saber, Monteiro Sildomar T., Cahill Nathan D., Messinger David W. Classification of hyperspectral images based on conditional random fields // Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 940510 (February 27, 2015); doi:10.1117/12.2083374; http://dx.doi.org/10.1117/12.2083374 7. Tarabalka Y., Rana A. Graph-Cut-Based Model for Spectral-Spatial Classification of Hyperspectral Images // International Geoscience and Remote Sensing Symposium (IGARSS); Quebec, Canada. 2014. P. 3418–21. 8. Lillesand M. T., Kiefer R. W., Chipman J. W. Remote Sensing and Image Interpretation. N. Y.: John Wiley & Sons, 2004. 763 p. 9. Hader D. P. Imageanalysis: methods and applications. London: CRC Press, 2000. 480 p. 10. Baumgardner M. F., Biehl L. L., Landgrebe D. A. 220 Band AVIRIS Hyperspectral Image Data Set: June 12, 1992 Indian Pine Test Site 3. Purdue University Research Repository. 2015. doi:10.4231/R7RX991C. 11. Борзов С. М., Потатуркин А. О., Потатуркин О. И., Федотов А. М. Исследование эффективности классификации гиперспектральных спутниковых изображений природных и антропогенных территорий // Автометрия. 2016. № 1. С. 3–14. 12. Green A. A., Berman M., Switzer P., Craig M. D. A transformation for ordering multispectral data in terms of image quality with implications for noise removal // IEEE Transactions on Geoscience and Remote Sensing. 1988. Vol. 26. No. 1. P. 65–74.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".