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, 487–492, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-487-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 487–492, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-487-2020

  06 Nov 2020

06 Nov 2020

CHARACTERIZATION OF SAR IMAGES WITH WEIGHTED AMPLITUDE TRANSITION GRAPHS

E. T. C. Chagas1, A. C. Frery2, O. A. Rosso3, and H. S. Ramos1 E. T. C. Chagas et al.
  • 1Dept. de Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
  • 2Laboratório de Computação Científica e Análise Numérica – LaCCAN, Universidade Federal de Alagoas, Brazil
  • 3Instituto de Física, Universidade Federal de Alagoas, Brasil, Instituto de Medicina Traslacional e Ingeniería Biomédica Hospital Italiano de Buenos Aires & Conicet, Argentina

Keywords: Synthetic Aperture Radar (SAR), Time-series, Terrain Classification, Permutation Entropy, Ordinal Patterns Transition Graphs, Causality Complexity-Entropy Plane

Abstract. We propose a new technique for SAR image texture characterization based on ordinal pattern transition graphs. The proposal consists in (i) transforming a 2-D patch of data into a time series using a Hilbert Space Filling Curve, (ii) building an Ordinal Pattern Transition Graph with weighted edges; (iii) obtaining a probability distribution function from this graph; (iv) computing the Entropy and Statistical Complexity of this distribution. The weight of the edges is related to the absolute difference of observations. This modification takes into account the scattering properties of the target, and leads to a good characterization of several types of textures. Experiments with data from Munich urban areas, Guatemala forest regions, and Cape Canaveral ocean samples demonstrate the effectiveness of our technique, which achieves satisfactory levels of separability.