geoskill-transfer-learning-rs

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

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

bash

pip install numpy rasterio scipy scikit-learn geopandas shapely

Usage / 使用方法

Example 1 (synthetic data, offline)

bash

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)

bash

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

bash

python geoskill-transfer-learning-rs.py --input scene.tif --n-classes 4 --output-dir ./out

Example 4: Random forest classification head

bash

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; --synthetic mode 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

bash

pip install numpy rasterio scipy scikit-learn geopandas shapely

使用方法

示例 1（合成数据，离线）

bash

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）

bash

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

bash

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

bash

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