Fuzzy Similarity Measure and its Application to High Resolution Colour Remote Sensing Image Processing
Bibliographic record
Abstract
The focus for the study in this thesis is placed on developing basic algorithms and tools for high-resolution colour remote sensing image processing tasks such as colour morphology, multivariate clustering, and multivariate filtering. First, the fuzzy similarity measure (FSM) among vectors in a vector space is introduced. This measure is based on two assumptions for the relationship among vectors: short-range ordering and fuzzification. Second, based on the FSM, the colour morphology, multivariate fuzzy clustering, and multivariate filtering are defined. The performances of all proposed methods will be evaluated numerically and subjectively. Third, this study also places more emphases on solving some applied problems related to recognizing colour edges, detecting and extracting complex road network and building rooftops, and reducing noise in high-resolution remote sensing images such as QuickBird, Ikonos, and aerial images. The results obtained in the study demonstrate the effectiveness and efficacy of the FMS and the proposed methods.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".