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Fusion of textural and spectral information for tree crop and other agricultural cover mapping with very-high resolution satellite images

Ursani Ahsan Ahmad, Kpalma Kidiyo, Lelong Camille, Ronsin Joseph. 2012. Fusion of textural and spectral information for tree crop and other agricultural cover mapping with very-high resolution satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5 (1) : pp. 225-235.

Journal article ; Article de revue à facteur d'impact
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Quartile : Q1, Sujet : REMOTE SENSING / Quartile : Q1, Sujet : IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY / Quartile : Q2, Sujet : GEOGRAPHY, PHYSICAL

Abstract : A new procedure is proposed for agricultural land-use mapping that addresses a known weakness of classical per-pixel methods in situations involving mixed tree crops. The proposed scheme uses a pair of very-high resolution satellite-borne panchromatic and multispectral images and integrates classification results of two parallel and independent analyses, respectively based on spectral and textural information. The multispectral image is divided into spectrally homogeneous but non-contiguous segments using unsupervised classification. In parallel, the panchromatic image is split into a grid of square blocks on which is performed a texture-driven supervised classification. Finally, the spectral and the textural classifications are fused to generate the land-use map. This method contrasts with object-based methods that sequentially perform image segmentation and classification. Results are evaluated both quantitatively and qualitatively, based on field survey ground-truth data. The quantitative assessment is presented in terms of overall accuracy (from 80% to 100% depending on the area) and Kappa coefficients. Visual comparison of the resulting map with the ground-truth is performed, with the analysis of the binary error maps. Merging spectral and textural classifications results in finer border delimitation and improves the overall classification accuracy of agricultural land-use by 27% as compared to textural classification alone. (Résumé d'auteur)

Mots-clés Agrovoc : Cartographie, Terre agricole, Télédétection

Classification Agris : U30 - Research methods
P31 - Soil surveys and mapping

Champ stratégique Cirad : Axe 6 (2005-2013) - Agriculture, environnement, nature et sociétés

Auteurs et affiliations

  • Ursani Ahsan Ahmad, Mehran University of Engineering and Technology (PAK)
  • Kpalma Kidiyo, INSA de Rennes (FRA)
  • Lelong Camille, CIRAD-ES-UMR TETIS (FRA)
  • Ronsin Joseph, INSA de Rennes (FRA)

Source : Cirad - Agritrop (https://agritrop.cirad.fr/563509/)

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