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PySizer: resizing raster in eigenspace using Python

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Abstract

A common practice in spatial analysis, data fusing, and geophysical interpretation is the need to handle raster datasets of different spatial resolution. Often, raster data are resampled through interpolation for better spatial representation and feature identification at both local and regional scales, but interpolation can result in artifact such as fewer blocks or mosaics, boundary blurring, edge halos, rings, and signal aliasing. In this study, we present the PySizer program developed using the Python programming language for data interpolation, with advanced performance. First, it has high interpolation accuracy by using the inverse spatial principal analysis (isPCA) in the eigenspace. Secondly, it has high calculation performance by using functions inside scientific Python modules, such as matrix operations of the NumPy. Thirdly, it supports most of currently available raster data formats and can manage spatial references information. PySizer is applicable to many fields, such as geophysical or other scientific data processing, remote sensing, (internet) video stream interpolation, and high quality image printing, to name a few. The tool was tested in this study using a potential-field data and bathymetric data, and the results were evaluated through visual inspection and statistical analysis, demonstrating high performance in accuracy and efficiency in resizing raster datasets. The PySizer source codes are freely available from public website or by contacting the authors for the latest version.

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Availability and Requirements

We develop the PySizer as an open source program following the GNU GENERAL PUBLIC LICENSE (GPL 2.0) and it can be freely downloaded in the public domain at https://github.com/QingmouLi/PySizer. Readers can also contact the communicating author to get the latest version.

We developed the PySizer using pure Python (CPython) so that it could run on any Python (2.7+) supported OS platforms, such as the MS Windows 7&10, Linux or Mac OS. However, we only test it on 64-bit MS Windows 7&10 and Ubuntu 18.02. We also widely use the NumPy (11.0.0), SciPy (0.13.3), and GDAL (2.0.2) in an Anaconda (version 5.3, Anaconda Inc 2019) virtual environment.

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Acknowledgements

Authors give thanks to Chief Editor Babaie and the anonymous reviewers for the support and suggestion of the manuscript. The authors specifically thank one of the reviewers for the significant update of the manuscript. The authors wish to acknowledge support from Natural Resources Canada through the Geo-Mapping for Energy and Minerals 2 (GEM2) program and Geological Survey of Canada (GSC) Innovation Micro-fund of Geoscience for Climate Change Program. This is the Natural Resources of Canada’s research contribution number: 20180431.

Author information

Authors and Affiliations

  1. Canada Hazards Information Service (CHIS), Natural Resources Canada, Ottawa, ON, Canada

    Qingmou Li

  2. Geological Survey of Canada (Calgary), Natural Resources Canada, Calgary, Alberta, Canada

    Sonya A. Dehler

Authors
  1. Qingmou Li
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  2. Sonya A. Dehler
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Corresponding author

Correspondence to Qingmou Li.

Additional information

Software files

A module file PySizer.py includes a class for handling the input/output, geo-transformation, spatial references, and non-value cell management for a GIS raster dataset. It also includes functions to calculate the singular spectrum (SSA), eigen-vectors, and loading of a raster, and to resize a raster using the isPCA method.

The SizeRaster.py is a demonstrating as well as a practical working program to do real raster resizing by calling the PySizer.py module. We honor POSIX command style for command arguments parsing and displaying help information, such as the input ‘-h’ will prompt the arguments format.

The readme.md gives more details and a data processing example.

We also include three additional sample data files, the Freeair gravity and magnetic anomalies, and bathymetry raster dataset files of the study area in the geotif (*.tif) file format, to levitate users’ starting curve

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Highlights

• PySizer program is developed for resizing spatial raster data

• PySizer has high calculation performance comparable with native codes using scientific scripting;

• PySizer can accept major raster formats and handle georeference and geotransformation information;

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Li, Q., Dehler, S.A. PySizer: resizing raster in eigenspace using Python. Earth Sci Inform 13, 191–204 (2020). https://doi.org/10.1007/s12145-019-00406-y

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