tominent / awesome-remote-sensing-change-detection

List of datasets, codes, researchers and contests related to remote sensing change detection

Awesome Remote Sensing Change Detection

List of datasets, codes, researchers and contests related to remote sensing change detection.The newest at the top of each category.

1. Dateset

1.1 Multispectral

• 2019.Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data

• 2019.Wuhan multi-temperature scene (MtS-WH) Dataset
  The dataset is mainly used for theoretical research and verification of scene change detection methods. It consists of two large-size VHR images, which have a size of 7200x6000 and are respectively acquired by IKONOS sensors in Feb, 2002 and Jun, 2009. The images cover the Hanyang District, Wuhan City, China and contain 4 spectral bands (Blue, Green, Red and Near-Infrared). The spatial resolution of the images is 1m after fusion of the pan and multispectral images by the Gram–Schmidt algorithm.

• 2018.Onera Satellite Change Detection Dataset
  This dataset addresses the issue of detecting changes between satellite images from different dates. It comprises 24 pairs of multispectral images taken from the Sentinel-2 satellites between 2015 and 2018. Locations are picked all over the world, in Brazil, USA, Europe, Middle-East and Asia. For each location, registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites are provided. Images vary in spatial resolution between 10m, 20m and 60m.

• 2017.Damage Detection from Aerial Images via Convolutional Neural Networks(ABCDdataset)
  This is a new labeled dataset, specially geared toward constructing and evaluating damage detection systems to identify whether buildings have been washed-away by tsunami.These pairs were cropped from a hefty number of RGB aerial images of Tohoku region of Japan. These aerial images were taken before or after the Great East Japan earthquake, with the original pixel resolution of 40 cm for pre-quake images and 12 cm for post-qukae images (actually, resampled to 40 cm).

• 2015.Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

• 2008.SZTAKI AirChange Benchmark set
  This Benchmark set contains 13 aerial image pairs of size 952x640 and resolution 1.5m/pixel and binary change masks (drawn by hand).Each record constains a pair of preliminary registered input images and a mask of the 'relevant' changes. The input images are taken with 5, 7 resp. 23 years time differences. During the generation of the change mask, we have considered the following differences as relevant changes: (a) new built-up regions (b) building operations (c) planting of large group of trees (d) fresh plough-land (e) groundwork before building over. Note that the ground truth does NOT contain change classification, only binary change-no change decision for each pixel.
  Note: This is probably the most frequently used dataset in the paper of remote sensing change detection.

• WUDA-RS-Img(wuhan university datasets of annotated remote sensing images)
  The dataset about change detection will be released in the future.

• DigitalGlobe's Open Data Program

  • haiti

• French National Institute of Geographical and Forest Information (IGN),bdortho

  • Urban Atlas vector

• LINZ DATA SERVICE

  • OpenStreetMap
  • OpenStreetMapHistory
  • Urban footprints: some building outline data sources
  • AIRS (Aerial Imagery for Roof Segmentation)

• ESA change detection(Landsat and Sentinel-2)

1.2 Hyperspectral

• 2018.Stacked autoencoders for multiclass change detection in hyperspectral images

• 2018.GETNET: A General End-to-End 2-D CNN Framework for Hyperspectral Image Change Detection

1.3 CV

• 2014.CDnet (video change detection)

• 2015.Change Detection from a Street Image Pair using CNN Features and Superpixel Segmentation

• 2018.Fully Convolutional Siamese Network for Scene Change Detection

2. Code

2.1. Multispectral

2.1.1 Traditional Method

• 2019.M J Canty. Image Analysis, Classification and Change Detection in Remote Sensing(Fourth Revised Edition)

• 2017.M J Canty. Change Detection with Google Earth Engine Imagery

• 2014.M J Canty. Image Analysis, Classification and Change Detection in Remote Sensing(Third Revised Edition)

• Zhu Zhe.Algorithm developed for Continuous Change Detection and Classification (CCDC) of land cover using all available Landsat data

• Implementation of " 2009.Celik T. Unsupervised change detection in satellite images using principal component analysis and k-means clustering ".
  Matlab,Python

2.1.2 Deep Learning

• Rodrigo Caye Daudt

  • 2019. Guided anisotropic diffusion (GAD)
  • 2018. Fully convolutional change detection
  • 2018. Patch based change detection

• 2019. Maria Papadomanolaki,et al. Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data(IGARSS 2019)

• 2019. DSFANet (Deep Slow Feature Analysis Network)

• 2019. End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

• 2018. Rahman F, Vasu B, Van Cor J, et al. Siamese Network with Multi-level Features for Patch-based Change Detection in Satellite Imagery

• 2018. Enqiang Guo, Xinsha Fu,et al.Learning to Measure Changes: Fully Convolutional Siamese Metric Networks for Scene Change Detection

• 2018. Shishira R Maiya,et al.Slum Segmentation and Change Detection : A Deep Learning Approach

• 2016. El Amin A M, Liu Q, Wang Y. Convolutional neural network features based change detection in satellite images

2.2 SAR

• Feng Gao

  • 2019. Sea ice change detection in SAR images based on convolutional-wavelet neural networks(Matlab)
  • 2019.Transferred deep learning for sea ice change detection from synthetic aperture radar images(Matlab)

• 2015. Knut Conradsen,et al.Change detection in polarimetric SAR images in Python

3. Researcher

3.1 Team

University of Trento

• RSLAB(Remote Sensing Laboratory) - Lorenzo Bruzzone

Wuhan University

• Sigma(Sensing Intelligence, Geoscience and MAchine learning lab) - Bo Du
• CAPTAIN(Computational and Photogrammetric Vision Team) - Gui-Song Xia
• CVEO(Computer Vision for Earth Observation team) - Xiaodong Zhang

Xidian University

• OMEGA(Optimization and Mining Knowledge via Artificial Intelligence) - Maoguo Gong

University of Connecticut

• GERS Lab(Global Environmental Remote Sensing Laboratory) - Zhe Zhu

4.Contest

• xView 2 Building Damage Asessment Challenge (DIUx, Nov 2019)
  550k building footprints & 4 damage scale categories, 20 global locations and 7 disaster types (wildfire, landslides, dam collapses, volcanic eruptions, earthquakes/tsunamis, wind, flooding), Worldview-3 imagery (0.3m res.), pre-trained baseline model. Paper: Gupta et al. 2019

• 遥感图像稀疏表征与智能分析竞赛-变化检测 (Wuhan University et al.Jul 2019)
  本项竞赛以光学遥感图像为处理对象，参赛队伍使用主办方提供的遥感图像进行建筑物变化检测，主办方根据评分标准对变化检测结果进行综合评价。竞赛中将提供两个不同时间获取的大尺度高分辨率遥感图像（包含蓝、绿、红和近红外四个波段），以及图像中变化区域的二值化标注数据集。

• 广东政务数据创新大赛—智能算法赛 (Alibaba et al.Nov 2017)
  使用2015年和2017年分别获取到的广东省某地的Quickbird卫星影像（包含蓝、绿、红和近红外四个波段），识别出两年之间新增的人工地上建筑物（不包括道路）。获胜团队的解决方案可以在天池官网上找到。

• Draper Satellite Image Chronology (Draper, Jun 2016)
  Predict the chronological order of images taken at the same locations over 5 days, Kaggle kernels

Reference

• Awesome Satellite Imagery Datasets
• Zhang Bin's Blog. remote sensing datasets
• The picture of this page is from Mou L et al.2019