Image Classification for Different Land Use and Land Covers Using Fuzzy Logic for the Improvement of Accuracies
Bibliographic record
Abstract
The aim of present research is to classify the satellite images of Ranchi area using fuzzy logic for different land use and land covers. An IRS-LISS III (Linear Imaging Self Scanning Sensor) image has been used for classification. Fuzzy logic is relatively a new theory. Now, fuzzy logic is widely used in the classification of remotely sensed images, for various land use and land cover classes. Classification of images includes pervious and impervious categories. Pervious categories contain mainly standing water bodies, natural vegetation and agricultural lands. Impervious categories contain dense built-up, moderate built-up and low density built-up area. The images of Ranchi area has been classified using standard maximum likelihood (ML) as well as fuzzy techniques using supervised method of classification using ERDAS IMAGINE 9.1. After classification of images, producer’s accuracy, user’s accuracy, overall accuracy and kappa coefficient values have been calculated with the help of confusion / error matrix. Result shows that in pervious category, standing water body exhibits highest accuracy (100%), then natural vegetation and agricultural land exhibits lowest accuracy. Standing water exhibits highest accuracy due to more clear pixels. Among the impervious categories, low density built-up area exhibits highest producer’s accuracy due to small area, dense built-up has second highest and moderate built-up has lowest producer’s accuracies. Comparison among accuracies have been done for both techniques and it is observed that the fuzzy logic is a better classification methodology than the standard ML method because overall accuracy and kappa value are higher for fuzzy classified 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".