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

simple demo codes for Learning to Teach with Dynamic Loss Functions

Learning to Teach with Dynamic Loss Functions This repo contains the simple demo for the NeurIPS-18 paper: Learning to Teach with Dynamic Loss Functio

15 Dec 30, 2021

Viperdb - A tiny log-structured key-value database written in pure Python

ViperDB 🐍 ViperDB is a lightweight embedded key-value store written in pure Pyt

17 Oct 17, 2022

Video-face-extractor - Video face extractor with Python

Python face extractor Setup Create the srcvideos and faces directories Put your

2 Feb 03, 2022

MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

187 Dec 26, 2022

🤗 Push your spaCy pipelines to the Hugging Face Hub

spacy-huggingface-hub: Push your spaCy pipelines to the Hugging Face Hub This package provides a CLI command for uploading any trained spaCy pipeline

30 Oct 09, 2022

Ἀνατομή is a PyTorch library to analyze representation of neural networks

50 Dec 05, 2022

A curated list of resources for Image and Video Deblurring

1.7k Jan 01, 2023

Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters"

Manga Character Screentone Synthesis Official PyTorch implementation of "Synthesis of Screentone Patterns of Manga Characters" presented in IEEE ISM 2

2 Nov 20, 2021

Language-Driven Semantic Segmentation

Language-driven Semantic Segmentation (LSeg) The repo contains official PyTorch Implementation of paper Language-driven Semantic Segmentation. Authors

416 Jan 03, 2023

EMNLP 2020 - Summarizing Text on Any Aspects

Summarizing Text on Any Aspects This repo contains preliminary code of the following paper: Summarizing Text on Any Aspects: A Knowledge-Informed Weak

35 Nov 14, 2022

PyTorch implementation of Octave Convolution with pre-trained Oct-ResNet and Oct-MobileNet models

octconv.pytorch PyTorch implementation of Octave Convolution in Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octa

273 Dec 18, 2022

Train Dense Passage Retriever (DPR) with a single GPU

Gradient Cached Dense Passage Retrieval Gradient Cached Dense Passage Retrieval (GC-DPR) - is an extension of the original DPR library. We introduce G

92 Jan 02, 2023

AbelNN: Deep Learning Python module from scratch

AbelNN: Deep Learning Python module from scratch I have implemented several neural networks from scratch using only Numpy. I have designed the module

2 Apr 12, 2022

Using this codebase as a tool for my own research. Making some modifications to the original repo for my own purposes.

For SwapNet Create a list.txt file containing all the images to process. This can be done with the GNU find command: find path/to/input/folder -name '

2 Nov 10, 2021

Machine learning framework for both deep learning and traditional algorithms

NeoML is an end-to-end machine learning framework that allows you to build, train, and deploy ML models. This framework is used by ABBYY engineers for

704 Dec 27, 2022

RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP

[Paper] [Хабр] [Model Card] [Colab] [Kaggle] RuDOLPH 🦌 🎄 ☃️ One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP Russian Diffusio

232 Jan 04, 2023

A unified 3D Transformer Pipeline for visual synthesis

Overview This is the official repo for the paper: NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion. NÜWA is a unified multimodal p

2.6k Jan 06, 2023

Interpretation of T cell states using reference single-cell atlases

Interpretation of T cell states using reference single-cell atlases ProjecTILs is a computational method to project scRNA-seq data into reference sing

139 Jan 03, 2023

A flexible ML framework built to simplify medical image reconstruction and analysis experimentation.

meddlr Getting Started Meddlr is a config-driven ML framework built to simplify medical image reconstruction and analysis problems. Installation To av

36 Dec 16, 2022

Code for Blind Image Decomposition (BID) and Blind Image Decomposition network (BIDeN).

arXiv, porject page, paper Blind Image Decomposition (BID) Blind Image Decomposition is a novel task. The task requires separating a superimposed imag

64 Dec 20, 2022