ISPRS Workshop on Updating Geo-spatial Databases with Imagery & The 5th ISPRS Workshop on DMGISs FUSION OF MODIS AND RADARSAT DATA FOR CROP TYPE CLASSIFICATION — AN INITIAL STUDY
Bibliographic record
Abstract
Agricultural land use mapping and change detection are important for environmental assessment and crop yield estimation. An effective mapping and change detection of crop land use requires large image coverage with sufficient resolutions in spatial, spectral and temporal dimensions. But few earth observation sensors can solely collect image data that meet all these requirements. The MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images provided by NASA are an optimal data source for collecting high resolution spectral and temporal information of the earth’s surface, because of its large coverage (2300 km ground swath), sufficient spectral bands (7 bands between visible and mid infrared at the spatial resolution of 250 to 500 m; and 28 bands between visible and thermal infrared at the spatial resolution of 1000 m), daily revisit rate, and low cost (free of charge). But its spatial resolutions (250 m, 500 m and 1000 m) are too coarse to delineate the crop field boundaries. On the other hand, the Radarsat images provided by the Canadian Space Agency are a good data source for obtaining high resolution spatial information (from 3 m to 100 m depending on the beam mode used) at very frequent repeat rate, because of its all-whether and day-night collection capability, low cost, and large coverage (from 50km×50km to 500km×500km depending on the beam mode). However, Radarsat images, like other radar images, are noisy and have limited spectral information. Even though field boundaries are recognizable in many beam modes, it is not effective to differentiate crop types just using a single radar image. To find a cost-effective solution for frequent, large coverage crop land use mapping, this paper presents an initial study on combination of low spatial resolution MODIS mulitspectral images and high spatial resolution Radarsat amplitude images for crop
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.028 |
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".