SciELO - Scientific Electronic Library Online

 
vol.30 número1A Systematic Review of Deep Learning Methods Applied to Ocular ImagesPatient-Specific Detection of Atrial Fibrillation in Segments of ECG Signals using Deep Neural Networks índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

  • Em processo de indexaçãoCitado por Google
  • Não possue artigos similaresSimilares em SciELO
  • Em processo de indexaçãoSimilares em Google

Compartilhar


Ciencia e Ingeniería Neogranadina

versão impressa ISSN 0124-8170versão On-line ISSN 1909-7735

Resumo

MARTIN, Laura; MEDINA, Javier  e  UPEGUI, Erika. Assessment of Image-Texture Improvement Applied to Unmanned Aerial Vehicle Imagery for the Identification of Biotic Stress in Espeletia. Case Study: Moorlands of Chingaza (Colombia). Cienc. Ing. Neogranad. [online]. 2020, vol.30, n.1, pp.27-44.  Epub 16-Ago-2020. ISSN 0124-8170.  https://doi.org/10.18359/rcin.3342.

Espeletia is one of the most representative endemic species of moorland ecosystems and is currently being affected by biotic stress. Meanwhile, the analysis of images obtained by means of unmanned aerial vehicle imagery has proved its usefulness in environmental monitoring activities. This work is aimed at establishing whether image-texture analysis applied to unmanned aerial vehicle imagery from Moorlands of Chingaza (Colombia) allows the identification of biotic stress in Espeletia. To this end, this study makes use of occurrence analysis, gray-level co-occurrence matrix, and Fourier transform. Identification of healthy/unhealthy Espeletia is conducted using maximum likelihood tests and support vector machines. The results are assessed based on overall accuracy, the kappa coefficient and Bhattacharyya distance. By combining spectral and image-texture information, it is shown that classification accuracy increases, reaching kappa coefficient values of 0.9824 and overall accuracy values of 99.51%.

Palavras-chave : biotic stress; Espeletia; maximum likelihood; support vector machine; texture measurements; unmanned aerial vehicles.

        · resumo em Espanhol     · texto em Inglês     · Inglês ( pdf )