this repository includes codes to implement the approach in the paper
"Early- and in-season crop type mapping without current-year ground truth: Generating labels from historical information via a topology-based approach" (DOI: 10.1016/j.rse.2022.112994)
Step 1. segment satellite imagery into image patches and build training, validation and testing dataset using Segmentation.py
Step 2. generate heat maps and their targets using Generate_heat_map.py
Step 3. (optional) visually check your type-I and type-II heat maps and make revisions if there's any
Step 4. train deep learning models using Execuation.py
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作物分类-早期识别-极大优化了传统分类器的泛化问题