Texture-based analysis of hydrographical basins with multispectral imagery

Pedro G. Bascoy; Alberto S. Garea; Dora B. Heras; Francisco Argüello; Alvaro Ordóñez

doi:10.1117/12.2532760

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21 October 2019 Texture-based analysis of hydrographical basins with multispectral imagery

Pedro G. Bascoy, Alberto S. Garea, Dora B. Heras, Francisco Argüello, Alvaro Ordóñez

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Proceedings Volume 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI; 111490Q (2019) https://doi.org/10.1117/12.2532760
Event: SPIE Remote Sensing, 2019, Strasbourg, France

Abstract

In this paper the problem of studying the presence of different vegetation species and artificial structures in the riversides by using multispectral remote sensing information is studied. The information provided contributes to control the water resources in a region in northern Spain called Galicia. The problem is solved as a supervised classification computed over five-band multispectral images obtained by an Unmanned Aerial Vehicle (UAV). A classification scheme based on the extraction of spatial, spectral and textural features previous to a hierarchical classification by Support Vector Machine (SVM) is proposed. The scheme extracts the spatial-spectral information by means of a segmentation algorithm based on superpixels and by computing morphological operations over the bands of the image in order to generate an Extended Morphological Profile (EMP). The texture features extracted help in the classification of vegetation classes as the spatial-spectral features for these classes are not discriminant enough. The classification is computed over segments instead of pixels, thus reducing the computational cost. The experimental results over four real multispectral datasets from Galician riversides show that the proposed scheme improves over a standard classification method achieving very high accuracy results.

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Pedro G. Bascoy, Alberto S. Garea, Dora B. Heras, Francisco Argüello, and Alvaro Ordóñez "Texture-based analysis of hydrographical basins with multispectral imagery", Proc. SPIE 11149, Remote Sensing for Agriculture, Ecosystems, and Hydrology XXI, 111490Q (21 October 2019); https://doi.org/10.1117/12.2532760

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