Semi-automatic normalization of multitemporal remote images based on vegetative pseudo-invariant features

PLoS One. 2014 Mar 6;9(3):e91275. doi: 10.1371/journal.pone.0091275. eCollection 2014.

Abstract

A procedure to achieve the semi-automatic relative image normalization of multitemporal remote images of an agricultural scene called ARIN was developed using the following procedures: 1) defining the same parcel of selected vegetative pseudo-invariant features (VPIFs) in each multitemporal image; 2) extracting data concerning the VPIF spectral bands from each image; 3) calculating the correction factors (CFs) for each image band to fit each image band to the average value of the image series; and 4) obtaining the normalized images by linear transformation of each original image band through the corresponding CF. ARIN software was developed to semi-automatically perform the ARIN procedure. We have validated ARIN using seven GeoEye-1 satellite images taken over the same location in Southern Spain from early April to October 2010 at an interval of approximately 3 to 4 weeks. The following three VPIFs were chosen: citrus orchards (CIT), olive orchards (OLI) and poplar groves (POP). In the ARIN-normalized images, the range, standard deviation (s. d.) and root mean square error (RMSE) of the spectral bands and vegetation indices were considerably reduced compared to the original images, regardless of the VPIF or the combination of VPIFs selected for normalization, which demonstrates the method's efficacy. The correlation coefficients between the CFs among VPIFs for any spectral band (and all bands overall) were calculated to be at least 0.85 and were significant at P = 0.95, indicating that the normalization procedure was comparably performed regardless of the VPIF chosen. ARIN method was designed only for agricultural and forestry landscapes where VPIFs can be identified.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Agriculture*
  • Automation
  • Crops, Agricultural / anatomy & histology
  • Imaging, Three-Dimensional*
  • Reference Standards
  • Remote Sensing Technology*
  • Software
  • Time Factors

Grants and funding

All the external funding of this research was provided by the Spanish Commission of Science and Technology (http://www.idi.mineco.gob.es/portal/site/MICINN/), Ministry of Economy and Competition, through the projects AGL2007-60926 and AGL2010-15506. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.