AGRICULTURAL LAND CLASSIFICATION BASED ON STATISTICAL ANALYSIS OF FULL POLARIMETRIC SAR DATA
Bibliographic record
Abstract
Abstract. The discrimination capability of Polarimetric Synthetic Aperture Radar (PolSAR) data makes them a unique source of information with a significant contribution in tackling problems concerning environmental applications. One of the most important applications of these data is land cover classification of the earth surface. These data type, make more detailed classification of phenomena by using the physical parameters and scattering mechanisms. In this paper, we have proposed a contextual unsupervised classification approach for full PolSAR data, which allows the use of multiple sources of statistical evidence. Expectation-Maximization (EM) classification algorithm is basically performed to estimate land cover classes. The EM algorithm is an iterative algorithm that formalizes the problem of parameters estimation of a mixture distribution. To represent the statistical properties and integrate contextual information of the associated image data in the analysis process we used Markov random field (MRF) modelling technique. This model is developed by formulating the maximum posteriori decision rule as the minimization of suitable energy functions. For select optimum distribution which adapts the data more efficiently we used Mellin transform which is a natural analytical tool to study the distribution of products and quotients of independent random variables. Our proposed classification method is applied to a full polarimetric L-band dataset acquired from an agricultural region in Winnipeg, Canada. We evaluate the classification performance based on kappa and overall accuracies of the proposed approach and compared with other well-known classic methods.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".