Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

Last update: Dec 13, 2022

Overview

Change is Everywhere
Single-Temporal Supervised Object Change Detection
in Remote Sensing Imagery

by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei Zhong

[Paper] [BibTeX]

This is an official implementation of STAR and ChangeStar in our ICCV 2021 paper Change is Everywhere: Single-Temporal Supervised Object Change Detection for High Spatial Resolution Remote Sensing Imagery.

We hope that STAR will serve as a solid baseline and help ease future research in weakly-supervised object change detection.

News

2021/08/28, The code is available.
2021/07/23, The code will be released soon.
2021/07/23, This paper is accepted by ICCV 2021.

Features

Learning a good change detector from single-temporal supervision.
Strong baselines for bitemporal and single-temporal supervised change detection.
A clean codebase for weakly-supervised change detection.
Support both bitemporal and single-temporal supervised settings

Citation

If you use STAR or ChangeStar (FarSeg) in your research, please cite the following paper:

@inproceedings{zheng2021change,
  title={Change is Everywhere: Single-Temporal Supervised Object Change Detection for High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Ma, Ailong and Liangpei Zhang and Zhong, Yanfei},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={},
  year={2021}
}

@inproceedings{zheng2020foreground,
  title={Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
  author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4096--4105},
  year={2020}
}

Getting Started

Install EVer

pip install --upgrade git+https://github.com/Z-Zheng/ever.git

Requirements:

pytorch >= 1.6.0
python >=3.6

Prepare Dataset

Download xView2 dataset (training set and tier3 set) and LEVIR-CD dataset.
Create soft link

ln -s </path/to/xView2> ./xView2
ln -s </path/to/LEVIR-CD> ./LEVIR-CD

Training and Evaluation under Single-Temporal Supervision

bash ./scripts/trainxView2/r50_farseg_changemixin_symmetry.sh

Training and Evaluation under Bitemporal Supervision

bash ./scripts/bisup_levircd/r50_farseg_changemixin.sh

License

ChangeStar is released under the Apache License 2.0.

Comments

Can ChangeStar be used for general CD?

hi,

Thanks for the great work. I wonder, can this work be used for general change detection? i.e., multi-class not just single class.

If yes, do you have done the experiments? Thanks!

opened by Richardych 3
hello, how to add changemixin when use bitemporal supervised
hello I have question about your repo:

how to add changeminxin when use bitemporal supervised, i see it in your paper table 4 but i cant find in codes?

could changestar use LEVIR-CD train Single-Temporal(another dataset is too big for train, i cant download it)

are your bitemporal suprvised methods just use torch.cat in the final layer? sorry for ask these question,
opened by csliuchang 3
ValueError: Requested crop size (512, 512) is larger than the image size (384, 384)

Traceback (most recent call last): File "./train_sup_change.py", line 48, in blob = trainer.run(after_construct_launcher_callbacks=[register_evaluate_fn]) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/ever/api/trainer/th_amp_ddp_trainer.py", line 117, in run test_data_loader=kw_dataloader['testdata_loader']) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/ever/core/launcher.py", line 232, in train_by_config signal_loss_dict = self.train_iters(train_data_loader, test_data_loader=test_data_loader, **config) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/ever/core/launcher.py", line 174, in train_iters is_master=self._master) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/ever/core/iterator.py", line 30, in next data = next(self._iterator) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 435, in next data = self._next_data() File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 475, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/torch/utils/data/dataset.py", line 218, in getitem return self.datasets[dataset_idx][sample_idx] File "/home/yujianzhi/tem/ChangeStar-master/data/levir_cd/dataset.py", line 30, in getitem blob = self.transforms(**dict(image=imgs, mask=gt)) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/albumentations/core/composition.py", line 191, in call data = t(force_apply=force_apply, **data) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/albumentations/core/transforms_interface.py", line 90, in call return self.apply_with_params(params, **kwargs) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/albumentations/core/transforms_interface.py", line 103, in apply_with_params res[key] = target_function(arg, **dict(params, **target_dependencies)) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/albumentations/augmentations/crops/transforms.py", line 48, in apply return F.random_crop(img, self.height, self.width, h_start, w_start) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/albumentations/augmentations/crops/functional.py", line 28, in random_crop crop_height=crop_height, crop_width=crop_width, height=height, width=width ValueError: Requested crop size (512, 512) is larger than the image size (384, 384) Traceback (most recent call last): File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/runpy.py", line 193, in _run_module_as_main "main", mod_spec) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/runpy.py", line 85, in _run_code exec(code, run_globals) File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/torch/distributed/launch.py", line 260, in main() File "/home/yujianzhi/anaconda3/envs/CStar/lib/python3.7/site-packages/torch/distributed/launch.py", line 256, in main cmd=cmd) subprocess.CalledProcessError: Command '['/home/yujianzhi/anaconda3/envs/CStar/bin/python', '-u', './train_sup_change.py', '--local_rank=0', '--config_path=levircd.r50_farseg_changestar_bisup', '--model_dir=./log/bisup-LEVIRCD/r50_farseg_changestar']' returned non-zero exit status 1.

it says： ValueError: Requested crop size (512, 512) is larger than the image size (384, 384) but my img is 512*512 exactly.

opened by themoongodyue 3
How to get the bitemporal images' labels if the model is trained on LEVIR-CD dataset?

Hello, I'm very interested in your work, but I encountered a problem in the process of research. If the model is trained on the LEVIR-CD dataset, how to obtain the changed labels when there are no segmentation maps for each bitemporal image in the dataset? I would appreciate it if you could solve my problems.

opened by SONGLEI-arch 2
Reproduction Problem

Hello author.

Your work is great!

But I ran into a problem while running your code.

The performance came as shown in the picture below, but this number is much higher than the number in table1 of your paper. (IoU) Can you tell me the reason?

All hyperparameters and data are identical.

opened by seominseok0429 1
AssertionError error

Hello, this is really great work. I have one question for you. The LEVIR-CD dataset trains well, but the xview2 dataset gives the following unknown error.

Do you have any idea how to fix it? All processes follow the recipe exactly .

opened by seominseok0429 1
RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:911, unhandled system error, NCCL version 2.7.8

i have crazy,help me please

Traceback (most recent call last): File "./train_sup_change.py", line 48, in blob = trainer.run(after_construct_launcher_callbacks=[register_evaluate_fn]) File "/home/cy/miniconda3/envs/STAnet/lib/python3.8/site-packages/ever/api/trainer/th_amp_ddp_trainer.py", line 98, in run kwargs.update(dict(model=self.make_model())) File "/home/cy/miniconda3/envs/STAnet/lib/python3.8/site-packages/ever/api/trainer/th_amp_ddp_trainer.py", line 87, in make_model model = nn.parallel.DistributedDataParallel( File "/home/cy/miniconda3/envs/STAnet/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 496, in init dist._verify_model_across_ranks(self.process_group, parameters) RuntimeError: NCCL error in: /pytorch/torch/lib/c10d/ProcessGroupNCCL.cpp:911, unhandled system error, NCCL version 2.7.8 ncclSystemError: System call (socket, malloc, munmap, etc) failed. ERROR:torch.distributed.elastic.multiprocessing.api:failed (exitcode: 1) local_rank: 0 (pid: 31335) of binary: /home/cy/miniconda3/envs/STAnet/bin/python ERROR:torch.distributed.elastic.agent.server.local_elastic_agent:[default] Worker group failed

opened by themoongodyue 1
Evaluation

Excuse me, I want to know how this module behave inference after training the model. And if you can offer an link for usage of 'ever' Lib, that will be fantastic

opened by LIUZIJING-CHN 1
changestar_sisup results

Hi, I have trained the model under single-temporal supervision, but the F1 result is only 0.73，which is worse than the result in your paper. Is there anything wrong with my experiment, below is my training log：

1666753326.225779.log

After training I only test the LEVIR-CD test set.

opened by max2857 0
A question about PCC

Hello,I have a question about PCC:

PCC is mentioned in the paper. After obtaining the classification result through the segmentation model, how to obtain the change detection result through the classification result? Is it a direct subtraction?

opened by Hyd1999618 0
[Feature] support [0~255] gt

The original dataset of LEVIR-CD consists of 0 and 255.

However, the segmentation loss of this code works only when it consists of 0 and 1.

Therefore, I added a code to change gt's 255 to 1.

opened by seominseok0429 1

Releases(v0.1.0)

v0.1.0(Nov 4, 2021)

The target files of xView2 tier3 should be generated from the given geojson files. For convenience, we release the generated target files here.
Source code(tar.gz)
Source code(zip)
xView2-tier3-targets.zip(22.63 MB)

Owner

Zhuo Zheng

CV IN RS. Ph.D. Student.

GitHub Repository

Meta-Learning Sparse Implicit Neural Representations (NeurIPS 2021)

Meta-SparseINR Official PyTorch implementation of "Meta-learning Sparse Implicit Neural Representations" (NeurIPS 2021) by Jaeho Lee*, Jihoon Tack*, N

41 Nov 10, 2022

[CVPR 2021] MiVOS - Scribble to Mask module

MiVOS (CVPR 2021) - Scribble To Mask Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] A simplistic network that turns scri

65 Dec 22, 2022

Code for Paper "Evidential Softmax for Sparse MultimodalDistributions in Deep Generative Models"

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models Abstract Many applications of generative models rely on the marginali

9 Jun 06, 2022

Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Dec 28, 2022

Request execution of Galaxy SARS-CoV-2 variation analysis workflows on input data you provide.

SARS-CoV-2 processing requests Request execution of Galaxy SARS-CoV-2 variation analysis workflows on input data you provide. Prerequisites This autom

17 Aug 13, 2022

FID calculation with proper image resizing and quantization steps

clean-fid: Fixing Inconsistencies in FID Project | Paper The FID calculation involves many steps that can produce inconsistencies in the final metric.

606 Jan 06, 2023

A way to store images in YAML.

YAMLImg A way to store images in YAML. I made this after seeing Roadcrosser's JSON-G because it was too inspiring to ignore this opportunity. Installa

5 Mar 14, 2022

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker

Example Of Fine-Tuning BERT For Named-Entity Recognition Task And Preparing For Cloud Deployment Using Flask, React, And Docker This repository contai

12 Dec 14, 2022

PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning.

neural-combinatorial-rl-pytorch PyTorch implementation of Neural Combinatorial Optimization with Reinforcement Learning. I have implemented the basic

454 Jan 06, 2023

Simple, but essential Bayesian optimization package

BayesO: A Bayesian optimization framework in Python Simple, but essential Bayesian optimization package. http://bayeso.org Online documentation Instal

74 Dec 05, 2022

efficient neural audio synthesis in the waveform domain

neural waveshaping synthesis real-time neural audio synthesis in the waveform domain paper • website • colab • audio by Ben Hayes, Charalampos Saitis,

169 Dec 23, 2022

Customer Segmentation using RFM

Customer-Segmentation-using-RFM İş Problemi Bir e-ticaret şirketi müşterilerini segmentlere ayırıp bu segmentlere göre pazarlama stratejileri belirlem

7 Dec 26, 2021

Answer a series of contextually-dependent questions like they may occur in natural human-to-human conversations.

SCAI-QReCC-21 [leaderboards] [registration] [forum] [contact] [SCAI] Answer a series of contextually-dependent questions like they may occur in natura

19 Sep 28, 2022

Differential fuzzing for the masses!

NEZHA NEZHA is an efficient and domain-independent differential fuzzer developed at Columbia University. NEZHA exploits the behavioral asymmetries bet

147 Dec 05, 2022

🦕 NanoSaur is a little tracked robot ROS2 enabled, made for an NVIDIA Jetson Nano

🦕 nanosaur NanoSaur is a little tracked robot ROS2 enabled, made for an NVIDIA Jetson Nano Website: nanosaur.ai Do you need an help? Discord For tech

162 Dec 09, 2022

Scrutinizing XAI with linear ground-truth data

This repository contains all the experiments presented in the corresponding paper: "Scrutinizing XAI using linear ground-truth data with suppressor va

2 Oct 04, 2022

Time-series-deep-learning - Developing Deep learning LSTM, BiLSTM models, and NeuralProphet for multi-step time-series forecasting of stock price.

Stock Price Prediction Using Deep Learning Univariate Time Series Predicting stock price using historical data of a company using Neural networks for

7 Nov 27, 2022