Remote sensing change detection using PaddlePaddle

Overview

Change Detection Laboratory

Developing and benchmarking deep learning-based remote sensing change detection methods using PaddlePaddle.

CDLab also has a PyTorch version. Currently, this repo contains more model implementations, dataset interfaces, and configuration files.

Prerequisites

opencv-python==4.1.1
paddlepaddle-gpu==2.2.0
visualdl==2.2.1
pyyaml==5.1.2
scikit-image==0.15.0
scikit-learn==0.21.3
scipy==1.3.1
tqdm==4.35.0

Tested using Python 3.7.4 on Ubuntu 16.04.

Get Started

In src/constants.py, change the dataset locations to your own.

Model Training

To train a model from scratch, use

python train.py train --exp_config PATH_TO_CONFIG_FILE

A few configuration files regarding different datasets and models are provided in the configs/ folder for ease of use.

As soon as the program starts and prints out the configurations, there will be a prompt asking you to write some notes. What you write will be recorded into the log file to help you remember what you did, or you can simply skip this step by pressing [Enter].

To resume training from some checkpoint, run the code with the --resume option.

python train.py train --exp_config PATH_TO_CONFIG_FILE --resume PATH_TO_CHECKPOINT

Other commandline options include:

  • --anew: Add it if the checkpoint is just used to initialize model weights. Note that loading an incompatible checkpoint is supported as a feature, which is useful when you are trying to utilize a well pretrained model for finetuning.
  • --save_on: By default, an epoch-based trainer is used for training. At the end of each training epoch, the trainer evaluates the model on the validation dataset. If you want to save the model output during the evaluation process, enable this option.
  • --log_off: Disable logging.
  • --vdl_on: Enable VisualDL summaries.
  • --debug_on: Useful when you are debugging your own code. In debugging mode, no checkpoint or model output will be written to disk. In addition, a breakpoint will be set where an unhandled exception occurs, which allows you to locate the causes of the crash or do some cleanup jobs.

During or after the training process, you can check the model weight files in exp/DATASET_NAME/weights/, the log files in exp/DATASET_NAME/logs, and the output change maps in exp/DATASET_NAME/out.

Model Evaluation

To evaluate a model on the test subset, use

python train.py eval --exp_config PATH_TO_CONFIG_FILE --resume PATH_TO_CHECKPOINT --save_on --subset test

Supported Architectures

Architecture Name Link
CDNet CDNet paper
FC-EF Unet paper
FC-Siam-conc SiamUnet-conc paper
FC-Siam-diff SiamUnet-diff paper
STANet STANet paper
DSIFN IFN paper
SNUNet SNUNet paper

Supported Datasets

Dataset Name Link
SZTAKI AirChange Benchmark set: Szada set AC-Szada website
SZTAKI AirChange Benchmark set: Tiszadob set AC-Tiszadob website
Onera Satellite Change Detection dataset OSCD website
Synthetic images and real season-varying remote sensing images SVCD google drive
LEVIR building change detection dataset LEVIRCD website
WHU building change detection dataset WHU website

This repository is currently under development. Note that no license has yet been added.

You might also like...
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

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

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 @

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

RetinaNet-PyTorch - A RetinaNet Pytorch Implementation on remote sensing images and has the similar mAP result with RetinaNet in MMdetection Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark
Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark

Universal Adversarial Examples in Remote Sensing: Methodology and Benchmark Yong

Official implement of Paper:A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sening  images
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

Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

scc4onnx Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel

Object detection and instance segmentation toolkit based on PaddlePaddle.
Object detection and instance segmentation toolkit based on PaddlePaddle.

Object detection and instance segmentation toolkit based on PaddlePaddle.

Owner
Lin Manhui
sluggish.
Lin Manhui
Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery (ICCV 2021)

Change is Everywhere Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery by Zhuo Zheng, Ailong Ma, Liangpei Zhang and Yanfei

Zhuo Zheng 125 Dec 13, 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
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
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
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
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
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
FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery (TGRS)

FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery by Ailong Ma, Junjue Wang*, Yanfei Zhon

Kingdrone 43 Jan 5, 2023
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