This is the core code of our work published in ISPRS J. 😘

This work propose an innovative assisted learning framework that employs a "teacher-student" architecture equipped with local and global distillation schemes for land use classification on heterogeneous data.

It has several advantages as outlined below:

⭐ Ability to maintain performance during testing with missing modalities

⭐ High interpretability demonstrating knowledge transferability between different modalities

⭐ Simplicity and flexibility

Below is the process of the proposed framework:

Step 1: Training teacher models using cross-entropy and dice loss. (Using train_t.py to train the teachr.)

Step 2: Training student models using our framework. (Using train_s.py to train the teachr.)

Step 3: Testing.