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

Related tags

Deep Learningcomputer-visiondeep-learningremote-sensingsegmentationchange-detection
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

  1. Download xView2 dataset (training set and tier3 set) and LEVIR-CD dataset.

  2. 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.

Copyright (c) Zhuo Zheng. All rights reserved.

Comments
  • Can ChangeStar be used for general CD?

    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, how to add changemixin when use bitemporal supervised

    hello I have question about your repo:

    1. how to add changeminxin when use bitemporal supervised, i see it in your paper table 4 but i cant find in codes?
    2. could changestar use LEVIR-CD train Single-Temporal(another dataset is too big for train, i cant download it)
    3. 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)

    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?

    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

    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? Screen Shot 2022-01-01 at 7 44 17 PM

    All hyperparameters and data are identical.

    opened by seominseok0429 1
  • AssertionError error

    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 Screen Shot 2021-12-31 at 4 57 41 PM .

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

    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

    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

    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

    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

    [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)
Owner
Zhuo Zheng
CV IN RS. Ph.D. Student.
Zhuo Zheng
GitHub Repository
The project is an official implementation of our paper "3D Human Pose Estimation with Spatial and Temporal Transformers".

3D Human Pose Estimation with Spatial and Temporal Transformers This repo is the official implementation for 3D Human Pose Estimation with Spatial and

Ce Zheng 363 Dec 28, 2022
CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

CIFAR-10_train-test - training and testing codes for dataset CIFAR-10

Frederick Wang 3 Apr 26, 2022
Implementation of the Point Transformer layer, in Pytorch

Point Transformer - Pytorch Implementation of the Point Transformer self-attention layer, in Pytorch. The simple circuit above seemed to have allowed

Phil Wang 501 Jan 03, 2023
Delving into Localization Errors for Monocular 3D Object Detection, CVPR'2021

Delving into Localization Errors for Monocular 3D Detection By Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, Wanli Ouyang. Intr

XINZHU.MA 124 Jan 04, 2023
Code I use to automatically update my videos' metadata on YouTube

mCodingYouTube This repository contains the code I use to automatically update my videos' metadata on YouTube, including: titles, descriptions, tags,

James Murphy 19 Oct 07, 2022
Gated-Shape CNN for Semantic Segmentation (ICCV 2019)

GSCNN This is the official code for: Gated-SCNN: Gated Shape CNNs for Semantic Segmentation Towaki Takikawa, David Acuna, Varun Jampani, Sanja Fidler

859 Dec 26, 2022
BEGAN in PyTorch

BEGAN in PyTorch This project is still in progress. If you are looking for the working code, use BEGAN-tensorflow. Requirements Python 2.7 Pillow tqdm

Taehoon Kim 260 Dec 07, 2022
Official Implementation of DDOD (Disentangle your Dense Object Detector), ACM MM2021

Disentangle Your Dense Object Detector This repo contains the supported code and configuration files to reproduce object detection results of Disentan

loveSnowBest 51 Jan 07, 2023
Neural Nano-Optics for High-quality Thin Lens Imaging

Neural Nano-Optics for High-quality Thin Lens Imaging Project Page | Paper | Data Ethan Tseng, Shane Colburn, James Whitehead, Luocheng Huang, Seung-H

Ethan Tseng 39 Dec 05, 2022
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's algorithm.

Bayes-Newton Bayes-Newton is a library for approximate inference in Gaussian processes (GPs) in JAX (with objax), built and actively maintained by Wil

AaltoML 165 Nov 27, 2022
Code for paper entitled "Improving Novelty Detection using the Reconstructions of Nearest Neighbours"

NLN: Nearest-Latent-Neighbours A repository containing the implementation of the paper entitled Improving Novelty Detection using the Reconstructions

Michael (Misha) Mesarcik 4 Dec 14, 2022
Research into Forex price prediction from price history using Deep Sequence Modeling with Stacked LSTMs.

Forex Data Prediction via Recurrent Neural Network Deep Sequence Modeling Research Paper Our research paper can be viewed here Installation Clone the

Alex Taradachuk 2 Aug 07, 2022
Koç University deep learning framework.

Knet Knet (pronounced "kay-net") is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU

1.4k Dec 31, 2022
Code for Understanding Pooling in Graph Neural Networks

Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install

Daniele Grattarola 37 Dec 13, 2022
Code for the paper "Reinforcement Learning as One Big Sequence Modeling Problem"

Trajectory Transformer Code release for Reinforcement Learning as One Big Sequence Modeling Problem. Installation All python dependencies are in envir

Michael Janner 269 Jan 05, 2023
Linear image-to-image translation

Linear (Un)supervised Image-to-Image Translation Examples for linear orthogonal transformations in PCA domain, learned without pairing supervision. Tr

Eitan Richardson 40 Aug 31, 2022
Fast, flexible and easy to use probabilistic modelling in Python.

Please consider citing the JMLR-MLOSS Manuscript if you've used pomegranate in your academic work! pomegranate is a package for building probabilistic

Jacob Schreiber 3k Dec 29, 2022
Transformer Tracking (CVPR2021)

TransT - Transformer Tracking [CVPR2021] Official implementation of the TransT (CVPR2021) , including training code and trained models. We are revisin

chenxin 465 Jan 06, 2023
Deep Learning Pipelines for Apache Spark

Deep Learning Pipelines for Apache Spark The repo only contains HorovodRunner code for local CI and API docs. To use HorovodRunner for distributed tra

Databricks 2k Jan 08, 2023
Pytorch Lightning Implementation of SC-Depth Methods.

SC_Depth_pl: This is a pytorch lightning implementation of SC-Depth (V1, V2) for self-supervised learning of monocular depth from video. In the V1 (IJ

JiaWang Bian 216 Dec 30, 2022