Ship Detection from RISAT-1 and Radarsat-2 SAR Images using CFAR
Bibliographic record
Abstract
Maritime surveillance has been an essential requirement since ancient times. However, in the absence of technology, it could not be done so effectively at that time as is being done in today's era of science and technology. The advancement in remote sensing technology has made maritime surveillance quicker and more precise. Ship detection and identification are playing a crucial role in the field of maritime surveillance in order to dealing with sea border activity, illegal fishery, maritime traffic, illegal migration of humans, navy movements or oil spill detection and monitoring. The information provided by imaging radar is fundamentally different from sensors that operate in infrared and visible portions of electromagnetic spectrum. SAR images are found to be very much suitable for the identification of sea objects because of very bright appearance of sea objects in SAR images against dark sea surface in background. In this work RISAT-1 SAR image of Mumbai offshore region (acquired on September 15, 2016) and Radarsat-2 SGF W2 mode of Vancouver, Canada (acquired on August 14, 2008) has been used for the rapid detection of ship objects using CFAR (Constant False Alarm Rate) algorithm technique provided by SNAP (Sentinel Application Platform) software. Presence of detected ship objects has also been demonstrated using kurtosis graph generated from azimuth and range FFT (Fast Fourier Transform) images of final ship detection resulted images of each study area.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".