Automatic identification of shrub vegetation of the Teide National Park

The identification of vegetation using remote sensing imagery is an important area of work with numerous applications such as the analysis of deforestation and crop monitoring. This work focuses on locating populations of an endemic shrub of the Canary Islands that grows in Teide National Park: the Teide broom. The objective is to determine automatically the location of this species in order to facilitate conservation efforts. For this purpose, a methodology is presented based on image analysis by using superpixels as the minimum image processing unit, combined with one-class classification. The proposed scheme has been analyzed by applying it to an area located at the northwest of the Teide National Park where the existence of populations of this shrub was known a priori.

[1]  FuaPascal,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012 .

[2]  Te-Ming Tu,et al.  A new look at IHS-like image fusion methods , 2001, Inf. Fusion.

[3]  Ernestina Menasalvas Ruiz,et al.  Local optimal scale in a hierarchical segmentation method for satellite images , 2016, Journal of Intelligent Information Systems.

[4]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[5]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[6]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Qi Zhang,et al.  Rolling Guidance Filter , 2014, ECCV.

[8]  Ernestina Menasalvas Ruiz,et al.  Local optimal scale in a hierarchical segmentation method for satellite image: an OBIA approach for the agricultural landscape , 2015 .

[9]  S. Süsstrunk,et al.  SLIC Superpixels ? , 2010 .

[10]  Consuelo Gonzalo-Martín,et al.  A GEOBIA Methodology for Fragmented Agricultural Landscapes , 2015, Remote. Sens..

[11]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[12]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[13]  Francesca Bovolo,et al.  Semisupervised One-Class Support Vector Machines for Classification of Remote Sensing Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.