liyemei / SCD-SAM

SCD-SAM

• The pytorch implementation for SCD-SAM in paper "SCD-SAM: Adapting Segment Anything Model for Semantic Change Detection in Remote Sensing Imagery".

Requirements

• Python 3.6
• Pytorch 1.7.0

Datasets Preparation

The path list in the datasest folder is as follows:

|—train

• ||—A

• ||—B

• ||—labelA

• ||—labelB

|—test

• ||—A

• ||—B

• ||—labelA

• ||—labelB

where A contains pre-temporal images, B contains post-temporal images, labelA contains pre-temporal ground truth images, and labelB contains post-temporal ground truth images.

Train

• python train.py --dataset-dir dataset-path

Test

• python eval.py --ckp-paths weight-path --dataset-dir dataset-path

Visualization

• python visualization visualization.py --ckp-paths weight-path --dataset-dir dataset-path (Note that batch-size must be 1 when using visualization.py)