Awesome

M-CD

This repository contains the code for "A Mamba-based Siamese Network for Remote Sensing Change Detection". Project page can be found here

Abstract

Change detection in remote sensing images is an essential tool for analyzing a region at different times. It finds varied applications in monitoring environmental changes, man-made changes as well as corresponding decision-making and prediction of future trends. Deep learning methods like Convolutional Neural Networks (CNNs) and Transformers have achieved remarkable success in detecting significant changes, given two images at different times. In this paper, we propose a Mamba-based Change Detector (M-CD) that segments out the regions of interest even better. Mamba-based architectures demonstrate linear-time training capabilities and an improved receptive field over transformers. Our experiments on four widely used change detection datasets demonstrate significant improvements over existing state-of-the-art (SOTA) methods.

Environment File

Create a new conda environment with the config file given in the repository as follows:

conda env create --file=m-cd.yml
conda activate m-cd

Install Mamba as follows:

cd models/encoders/selective_scan && pip install . && cd ../../..

General file descriptions

• configs/*.py - config files which control multiple parameters related to data training, logging etc.
• dataloader/changeDataset.py - dataset class defined here.
• models/* - model files available here
• train.py - driver file for training. Instructions below
• eval.py - driver file for evaluation. Instructions below

Link to model checkpoints

You can find the dataset wise checkpoints here

Datasets

1. We test our models on four public Change Detection datasets:

   • DSIFN-CD
   • LEVIR-CD
   • WHU-CD
   • CDD

   Please refer to the original dataset websites for more details. The links above containg the preprocessed splits, obtained from DDPM-CD

2. If you are using your own datasets, please orgnize the dataset folder in the following structure:

   <root_folder>
   |-- A
       |-- <name1>.png
       |-- <name2>.png
       ...
   |-- B
       |-- <name1>.png
       |-- <name2>.png
       ...
   |-- gt
       |-- <name1>.png
       |-- <name2>.png
       ...
   |-- list
       |-- train.txt
       |-- val.txt
       |-- test.txt
   

   train.txt/val.txt/test.txt contains the names of items in training/validation/testing set, e.g.:

   <name1>
   <name2>
   ...
   

Please make sure to change the root folder in the config files available in the folder "configs". Also, if the files are in a format other than png, please specify the extension in the config.

For custom datasets, you would need to create a config file similar to the existing files in "config" folder.

Training

1. Please download the pretrained VMamba weights:

   • VMamba_Tiny.
   • VMamba_Small.
   • VMamba_Base.

   <u> Please put them under pretrained/vmamba/. </u>

2. Config setting.

   Edit config file in the configs folder.
   Change C.backbone to sigma_tiny / sigma_small / sigma_base to use the three versions of Sigma.

3. Run multi-GPU distributed training:

   NCCL_P2P_DISABLE=1 CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node=4  --master_port 29502 train.py -p 29502 -d 0,1,2,3 -n "dataset_name"
   

   Here, dataset_name=dsifn/cdd/whu/levir, referring to the four datasets.

4. You can also use single-GPU training:

   CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" torchrun -m --nproc_per_node=1 train.py -p 29501 -d 0 -n "dataset_name" 
   

5. Results will be saved in log_final folder.

Evaluation

1. Run the evaluation by:

   CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python eval.py -d="0" -n "dataset_name" -e="epoch_number" -p="visualize_savedir"
   

   Here, dataset_name=dsifn/cdd/whu/levir, referring to the four datasets.
   epoch_number refers to a number standing for the epoch number you want to evaluate with. You can also use a .pth checkpoint path directly for epoch_number to test for a specific weight.

2. If you want to use multi GPUs please specify multiple Device IDs:

   CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python eval.py -d="0,1,2,3,4,5,6,7" -n "dataset_name" -e="epoch_number" -p="visualize_savedir"
   

3. Results will be saved in log_final folder.

Acknowledgements

Our code is adapted from Sigma. We thanks the authors for their valuable contribution and well-written code. The data splits and preprocessing are taken from DDPM-CD. We thank the authors for motivating this topic and facilitating further research in this topic by providing easy access to the datasets.

Citation

To be added