The Dataset

The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates.

Instructions: 

Onera Satellite Change Detection dataset

 

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Authors: Rodrigo Caye Daudt, rodrigo.daudt@onera.fr

Bertrand Le Saux, bls@ieee.org

Alexandre Boulch, alexandre.boulch@valeo.ai

Yann Gousseau, yann.gousseau@telecom-paristech.fr

 

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About: This dataset contains registered pairs of 13-band multispectral satellite images obtained by the Sentinel-2 satellites of the Copernicus program. Pixel-level urban change groundtruth is provided. In case of discrepancies in image size, the older images with resolution of 10m per pixel is used. Images vary in spatial resolution between 10m, 20m and 60m. For more information, please refer to Sentinel-2 documentation.

 

For each location, folders imgs_1_rect and imgs_2_rect contain the same images as imgs_1 and imgs_2 resampled at 10m resolution and cropped accordingly for ease of use. The proposed split into train and test images is contained in the train.txt and test.txt files.

For downloading and cropping the images, the Medusa toolbox was used: https://github.com/aboulch/medusa_tb

For precise registration of the images, the GeFolki toolbox was used. https://github.com/aplyer/gefolki

 

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Labels: The train labels are available in two formats, a .png visualization image and a .tif label image. In the png image, 0 means no change and 255 means change. In the tif image, 0 means no change and 1 means change.

<ROOT_DIR>//cm/ contains: - cm.png - -cm.tif

Please note that prediction images should be formated as the -cm.tif rasters for upload and evaluation on DASE (http://dase.grss-ieee.org/).

(Update June 2020) Alternatively, you can use the test labels which are now provided in a separate archive, and compute standard metrics using the python notebook provided in this repo, along with a fulll script to train and classify fully-convolutional networks for change detection: https://github.com/rcdaudt/fully_convolutional_change_detection 

 

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Citation: If you use this dataset for your work, please use the following citation:

@inproceedings{daudt-igarss18,

author = {{Caye Daudt}, R. and {Le Saux}, B. and Boulch, A. and Gousseau, Y.},

title = {Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks},

booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS'2018)},

venue = {Valencia, Spain},

month = {July},

year = {2018},

}

 

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Copyright: Sentinel Images: This dataset contains modified Copernicus data from 2015-2018. Original Copernicus Sentinel Data available from the European Space Agency (https://sentinel.esa.int).

Change labels: Change maps are released under Creative-Commons BY-NC-SA. For commercial purposes, please contact the authors.

 

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Instructions: 

Please refer to attached file "ZhangGRL_archive_submit_format.pdf" for a description of the data format and units. 

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Instructions: 

Instructions are given in the attached pdf file.

 

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Instructions: 

Python code and instructions for using the dataset are available in this repository: https://github.com/abhineet123/river_ice_segmentation

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Empirical line methods (ELM) are frequently used to correct images from aerial remote sensing. Remote sensing of aquatic environments captures only a small amount of energy because the water absorbs much of it. The small signal response of the water is proportionally smaller when compared to the other land surface targets.

 

This dataset presents some resources and results of a new approach to calibrate empirical lines combining reference calibration panels with water samples. We optimize the method using python algorithms until reaches the best result.

 

Instructions: 

The files are identified sequentially according to the processing step:

 

  • A1-img-nd_samples.xlsx: Digital numbers of water samples extract from the hyperspectral image
  • A2-img-nd_targets.xlsx: Digital numbers of reference targets extract from the hyperspectral image
  • B1-asd-rad_refl_targets.xlsx: Radiance values collected with ASD HandHeld of the reference targets and calculated Reflectance
  • B2-asd-simulatedbands_refl.xlsx: Target reflectance values calculated and simulated to match the hyperspectral camera response function
  • C1-trios-rad_refl_samples.xlsx: Radiance values collected with TriOS of the water points and calculated Reflectance
  • C2-trios-simulatedbands_refl.xlsx: Water reflectance values calculated and simulated to match the hyperspectral camera response function
  • D1-nd_data.csv: Digital number extracted from the hyperspectral image (CSV format, this is the input of the algorithm)
  • D1-nd_data.xlsx: Digital number extracted from the hyperspectral image (xlsx format)
  • D2-r_data.csv: Reflectance calculated from the spectroradiometers measurements (CSV format, this is the input of the algorithm)
  • D2-r_data.xlsx: Reflectance calculated from the spectroradiometers measurements (xlsx format)
  • D3-r_nd_targets.xlsx: Agregation from D1 and D2 data to compare the data
  • E1-calc_coef_line.py: Python algorithm to calibrate and validate the empirical line model
  • Fit.py: Python script class to calculate the Fit of linear and exponential function
  • output_graphs.zip: The results of the graphs generated for each of the evaluated combinations. In this package are different graphical representations for each of the combinations of samples and targets, as well as for the exponential and linear fits.

 

All files of the output folder are self-explained, because the filename identifies how the ELM was calibrated.

 

Details and descriptions about the full process steps are in the official paper (under journal review).

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