The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 91–95, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-91-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 91–95, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-91-2020

  04 Nov 2020

04 Nov 2020

AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS

A. R. Soares, T. S. Körting, L. M. G. Fonseca, and A. K. Neves A. R. Soares et al.
  • Coordination of Earth Observation (OBT) - Brazil’s National Institute for Space Research (INPE), Av. dos Astronautas, 1758, São José dos Campos - SP, Brazil

Keywords: Image segmentation, Remote Sensing, Conditional Random Fields, GEOBIA

Abstract. Segmentation is a fundamental problem in image processing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based Image Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory. The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2 m of spatial resolution. Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation.