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

Related tags

Deep Learning computer-vision deep-learning remote-sensing segmentation change-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
Official Pytorch Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images.

IAug_CDNet Official Implementation of Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images. Overview We propose a

null 53 Dec 2, 2022
Deep learning models for change detection of remote sensing images

Change Detection Models (Remote Sensing) Python library with Neural Networks for Change Detection based on PyTorch. ⚡ ⚡ ⚡ I am trying to build this pr

Kaiyu Li 176 Dec 24, 2022
Remote sensing change detection tool based on PaddlePaddle

PdRSCD PdRSCD（PaddlePaddle Remote Sensing Change Detection）是一个基于飞桨PaddlePaddle的遥感变化检测的项目，pypi包名为ppcd。目前0.2版本，最新支持图像列表输入的训练和预测，如多期影像、多源影像甚至多期多源影像。可以快速完

null 38 Aug 31, 2022
From this paper "SESNet: A Semantically Enhanced Siamese Network for Remote Sensing Change Detection"

SESNet for remote sensing image change detection It is the implementation of the paper: "SESNet: A Semantically Enhanced Siamese Network for Remote Se

null 1 May 24, 2022
Remote sensing change detection using PaddlePaddle

Change Detection Laboratory Developing and benchmarking deep learning-based remo

Lin Manhui 15 Sep 23, 2022
Official implement of Paper：A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening images

A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images 深度监督影像融合网络DSIFN用于高分辨率双时相遥感影像变化检测 Of

Chenxiao Zhang 135 Dec 19, 2022
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing Images

CFC-Net This project hosts the official implementation for the paper: CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Dete

ming71 55 Dec 12, 2022
a practicable framework used in Deep Learning. So far UDL only provide DCFNet implementation for the ICCV paper (Dynamic Cross Feature Fusion for Remote Sensing Pansharpening)

UDL UDL is a practicable framework used in Deep Learning (computer vision). Benchmark codes, results and models are available in UDL, please contact @

Xiao Wu 11 Sep 30, 2022
Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Feng 2 Nov 19, 2021
Hybrid CenterNet - Hybrid-supervised object detection / Weakly semi-supervised object detection

Hybrid-Supervised Object Detection System Object detection system trained by hybrid-supervision/weakly semi-supervision (HSOD/WSSOD): This project is

null 5 Dec 10, 2022
PyTorch implementation of popular datasets and models in remote sensing

PyTorch Remote Sensing (torchrs) (WIP) PyTorch implementation of popular datasets and models in remote sensing tasks (Change Detection, Image Super Re

isaac 222 Dec 28, 2022
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation (NeurIPS2021 Benchmark and Dataset Track)

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Kingdrone 174 Dec 22, 2022
paper: Hyperspectral Remote Sensing Image Classification Using Deep Convolutional Capsule Network

DC-CapsNet This is a tensorflow and keras based implementation of DC-CapsNet for HSI in the Remote Sensing Letters R. Lei et al., "Hyperspectral Remot

LEI 7 Nov 29, 2022
rastrainer is a QGIS plugin to training remote sensing semantic segmentation model based on PaddlePaddle.

rastrainer rastrainer is a QGIS plugin to training remote sensing semantic segmentation model based on PaddlePaddle. UI TODO Init UI. Add Block. Add l

deepbands 5 Mar 4, 2022
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation

LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation by Junjue Wang, Zhuo Zheng, Ailong Ma, Xiaoyan Lu, and Yanfei Zh

Payphone 8 Nov 21, 2022
RetinaNet-PyTorch - A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection

?? RetinaNet Horizontal Detector Based PyTorch This is a horizontal detector Ret

Fang Zhonghao 13 Nov 19, 2022
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark Yong

null 19 Dec 17, 2022
Awesome Remote Sensing Toolkit based on PaddlePaddle.

基于飞桨框架开发的高性能遥感图像处理开发套件，端到端地完成从训练到部署的全流程遥感深度学习应用。 最新动态 PaddleRS 即将发布alpha版本！欢迎大家试用 简介 PaddleRS是遥感科研院所、相关高校共同基于飞桨开发的遥感处理平台，支持遥感图像分类，目标检测，图像分割，以及变化检测等常用遥

null 146 Dec 11, 2022
Code for the paper "Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds" (ICCV 2021)

Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds This is the official code implementation for the paper "Spatio-temporal Se

Hesper 63 Jan 5, 2023