Land cover classification of RADARSAT-2 SAR data using convolutional neural network
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a convolutional neural network (CNN) based on deep learning method for land cover classification of synthetic aperture radar (SAR) images. The proposed method consists of convolutional layers, pooling layers, a full connection layer and an output layer. The method acquires high-level abstractions for SAR data by using a hierarchical architecture composed of multiple non-linear transformations such as convolutions and poolings. The feature maps produced by convolutional layers are subsampled by pooling layers and then are converted into a feature vector by the full connection layer. The feature vector is then used by the output layer with softmax regression to perform land cover classification. The multi-layer method replaces hand-engineered features with backpropagation (BP) neural network algorithm for supervised feature learning, hierarchical feature extraction and land cover classification of SAR images. RADARSAT-2 ultra-fine beam high resolution HH-SAR images acquired in the rural urban fringe of the Greater Toronto Area (GTA) are selected for this study. The experiment results show that the accuracy of our classification method is about 90% which is higher than that of nearest neighbor (NN).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it