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CSSM

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CSSM

Efficient Remote Sensing Change Detection with Change State Space Models

E.Ghazaei, E.Aptoula

Faculty of Engineering and Natural Sciences (VPALab), Sabanci University, Istanbul, Turkiye

[Paper Link]

🛎️Updates

Notice🐍🐍: CSSM has been accepted by IEEE GRSL! We'd appreciate it if you could give this repo a ⭐️star⭐️ and stay tuned!!
Nov 05th, 2025: The CSSM model and training code uploaded. You are welcome to use them!!

🚀 Overview

CSSM serves as an efficient and state-of-the-art (SOTA) benchmark for binary change detection.

📦 Requirements

bash

pip install torch torchvision torchaudio
pip install  pillow
pip install numpy scipy pandas
pip install matplotlib seaborn
pip install einops 
pip install torchinfo

📁 Dataset Preparation

This project supports three main change detection datasets:

LEVIR-CD+ - Download
SYSU-CD - Download
WHU-CD - Download

Dataset Structure

For LEVIR-CD:

your_dataset/
├── train/
│   ├── A/          # Pre-change images
│   ├── B/          # Post-change images
│   └── label/      # Ground truth masks
├── test/
│   ├── A/
│   ├── B/
│   └── label/
└── val/
    ├── A/
    ├── B/
    └── label/

For SYSU-CD:

your_dataset/
├── train/
│   ├── time1/          # Pre-change images
│   ├── time2/          # Post-change images
│   └── label/      # Ground truth masks
├── test/
│   ├── time1/
│   ├── time2/
│   └── label/
└── val/
    ├── time1/
    ├── time2/
    └── label/

For WHU-CD:

WHU-CD/
├── A/              # Pre-change images
├── B/              # Post-change images
├── label/          # Ground truth masks
├── train_list.txt  # List of training samples
├── test_list.txt   # List of test samples
└── val_list.txt    # List of validation samples

The text files should contain image names (one per line):

image_001.png
image_002.png
image_003.png
...

🚂 Training

LEVIR-CD Dataset

bash

python main.py \
    --dataset levir \
    --train_path /path/to/LEVIR-CD/train \
    --test_path /path/to/LEVIR-CD/test \
    --val_path /path/to/LEVIR-CD/val \
    --batch_size 64 \
    --epochs 50 \
    --lr 0.001

SYSU-CD Dataset

bash

python main.py \
    --dataset sysu \
    --train_path /path/to/SYSU-CD/train \
    --test_path /path/to/SYSU-CD/test \
    --val_path /path/to/SYSU-CD/val \
    --batch_size 32 \
    --epochs 100 \
    --lr 0.0001

WHU-CD Dataset

bash

python main.py \
    --dataset whu \
    --train_path /path/to/WHU-CD \
    --train_txt /path/to/train_list.txt \
    --test_txt /path/to/test_list.txt \
    --val_txt /path/to/val_list.txt \
    --batch_size 64 \
    --epochs 50

⚙️ Arguments

Required Arguments

Argument	Description	Example
`--dataset`	Dataset type: `levir`, `sysu`, or `whu`	`--dataset levir`
`--train_path`	Path to training data	`--train_path /data/train`
`--test_path`	Path to test data (not for WHU)	`--test_path /data/test`
`--val_path`	Path to validation data (not for WHU)	`--val_path /data/val`

WHU-CD Specific Arguments

Argument	Description	Example
`--train_txt`	Training sample list file	`--train_txt train_list.txt`
`--test_txt`	Test sample list file	`--test_txt test_list.txt`
`--val_txt`	Validation sample list file	`--val_txt val_list.txt`

Optional Arguments

Argument	Default	Description
`--batch_size`	64	Batch size for training
`--epochs`	50	Number of training epochs
`--lr`	0.001	Learning rate
`--step_size`	10	Learning rate scheduler step size
`--save_dir`	./checkpoints	Directory to save model checkpoints
`--model_name`	best_model.pth	Filename for saved model
`--seed`	42	Random seed for reproducibility
`--num_workers`	4	Number of data loading workers

🔧 Advanced Usage Examples

Custom Save Directory and Model Name

bash

python main.py \
    --dataset levir \
    --train_path /data/LEVIR-CD/train \
    --test_path /data/LEVIR-CD/test \
    --val_path /data/LEVIR-CD/val \
    --save_dir ./experiments/levir_exp1 \
    --model_name levir_model.pth \
    --epochs 100

Different Learning Rate Schedule

bash

python main.py \
    --dataset sysu \
    --train_path /data/SYSU-CD/train \
    --test_path /data/SYSU-CD/test \
    --val_path /data/SYSU-CD/val \
    --lr 0.0005 \
    --step_size 20 \
    --epochs 150

Smaller Batch Size (for limited GPU memory)

bash

python main.py \
    --dataset levir \
    --train_path /data/train \
    --test_path /data/test \
    --val_path /data/val \
    --batch_size 16 \
    --num_workers 2

📤 Output

During training, the script will:

Display training loss for each batch
Show validation metrics (IoU, confusion matrix) after each epoch
Save the best model based on validation IoU
Display learning rate and epoch time

Model Checkpoint

The best model is automatically saved to:

{save_dir}/{model_name}

Default: ./checkpoints/best_model.pth

🔍 Troubleshooting

Paths with Spaces

If your paths contain spaces, wrap them in quotes:

bash

python main.py \
    --dataset levir \
    --train_path "/path/with spaces/train" \
    --test_path "/path/with spaces/test" \
    --val_path "/path/with spaces/val"

CUDA Out of Memory

Reduce batch size:

python main.py --dataset levir ... --batch_size 16

Missing WHU Text Files

For WHU dataset, ensure all three text files are provided:

bash

python main.py \
    --dataset whu \
    --train_path /data/WHU-CD \
    --train_txt train_list.txt \
    --test_txt test_list.txt \
    --val_txt val_list.txt

💡 Getting Help

View all available arguments:

python main.py --help

📧 Contact

If you have any questions, please contact Elman Ghazaei at elman.ghazaei@sabanciuniv.edu

Qualitative Analysis:

Results

ElmanGhazaei/CSSM

CSSM

🛎️Updates

🚀 Overview

📦 Requirements

📁 Dataset Preparation

Dataset Structure

For LEVIR-CD:

For SYSU-CD:

For WHU-CD:

🚂 Training

LEVIR-CD Dataset

SYSU-CD Dataset

WHU-CD Dataset

⚙️ Arguments

Required Arguments

WHU-CD Specific Arguments

Optional Arguments

🔧 Advanced Usage Examples

Custom Save Directory and Model Name

Different Learning Rate Schedule

Smaller Batch Size (for limited GPU memory)

📤 Output

Model Checkpoint

🔍 Troubleshooting

Paths with Spaces

CUDA Out of Memory

Missing WHU Text Files

💡 Getting Help

📧 Contact

Qualitative Analysis:

Results

ElmanGhazaei/
CSSM