遥感迁移学习 | Remote Sensing Transfer Learning
Extracts features from imagery with a frozen feature extractor, fine-tunes a lightweight classification head on top, evaluates accuracy on a held-out validation set, and compares against a "raw-spectra-only" baseline to quantify the gain from transferred features.
This skill is an offline numpy-equivalent implementation of deep transfer learning (pretrained backbone + fine-tuned head): with no torch/tensorflow dependency, a fixed filter bank (raw spectra + Sobel gradients + local-mean texture) serves as the "frozen backbone", and an sklearn logistic regression / random forest serves as the "classification head"; the train/validation split, leak-free standardization, and transfer-gain comparison are all verified in unit tests.
Dependencies / 依赖
pip install numpy rasterio scipy scikit-learn geopandas shapely
Usage / 使用方法
Example 1 (synthetic data, offline)
python geoskill-transfer-learning-rs.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
Example 2: Synthetic supervised transfer evaluation (offline)
python geoskill-transfer-learning-rs.py --bbox 116.0 39.0 117.0 40.0 --synthetic --train-frac 0.6 --output-dir ./out
Example 3: Unsupervised feature clustering of a real image
python geoskill-transfer-learning-rs.py --input scene.tif --n-classes 4 --output-dir ./out
Example 4: Random forest classification head
python geoskill-transfer-learning-rs.py --bbox 116.0 39.0 117.0 40.0 --synthetic --model rf --output-dir ./out
Output / 输出
| File | Format | Description |
|---|---|---|
classification.tif |
GeoTIFF | Full-scene classification / clustering label map |
accuracy_report.json |
JSON | Validation accuracy, baseline comparison, and transfer gain |
output-manifest.json |
JSON | Run manifest (inputs/outputs/QA/exit code) |
Data Source / 数据源 / Source
Local multi-band GeoTIFF (unsupervised clustering in real mode), or a --synthetic three-class ground-truth scene (supervised evaluation).
Privacy / 隐私声明 / Privacy
- Runs offline by default;
--syntheticmode requires no network at all. - All processing is performed locally; no user data is uploaded.
License / License
MIT
name: geoskill-transfer-learning-rs description: '特征提取+微调分类器+精度评估，输出分类结果与精度报告（离线numpy等价实现）'
遥感迁移学习 | Remote Sensing Transfer Learning
用冻结的特征提取器从影像抽取特征，在其上微调轻量分类头，并在留出验证集上评估精度，同时与"仅用原始光谱"的基线对比，量化迁移特征的增益。
本 skill 是深度迁移学习（预训练主干 + fine-tune 头）的离线 numpy 等价实现：不依赖 torch/tensorflow，用固定滤波 bank（原始光谱 + Sobel 梯度 + 局部均值纹理）充当"冻结主干"，sklearn 逻辑回归/随机森林充当"分类头"；训练/验证划分、防泄漏标准化、迁移增益对比均在单元测试中验证。
依赖
pip install numpy rasterio scipy scikit-learn geopandas shapely
使用方法
示例 1（合成数据，离线）
python geoskill-transfer-learning-rs.py --bbox 116.0 39.0 117.0 40.0 --synthetic --output-dir ./out
示例 2：合成有监督迁移评估（离线）
python geoskill-transfer-learning-rs.py --bbox 116.0 39.0 117.0 40.0 --synthetic --train-frac 0.6 --output-dir ./out
示例 3：真实影像无监督特征聚类
python geoskill-transfer-learning-rs.py --input scene.tif --n-classes 4 --output-dir ./out
示例 4：随机森林分类头
python geoskill-transfer-learning-rs.py --bbox 116.0 39.0 117.0 40.0 --synthetic --model rf --output-dir ./out
输出
| 文件 | 格式 | 说明 |
|---|---|---|
classification.tif |
GeoTIFF | 全幅分类/聚类标签图 |
accuracy_report.json |
JSON | 验证精度、基线对比与迁移增益 |
output-manifest.json |
JSON | 运行清单（输入/输出/QA/退出码） |
数据源 / Source
本地多波段 GeoTIFF（真实模式无监督聚类），或 --synthetic 三类真值场景（有监督评估）。
隐私声明 / Privacy
- 默认离线运行，
--synthetic模式完全无网络。 - 所有处理在本地完成，不上传任何用户数据。
License
MIT