Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach-Reference-Cited by-同舟云学术

Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach

Published:2019-01-02 Issue:1 Volume:45 Page:16-25
ISSN:0703-8992
Container-title:Canadian Journal of Remote Sensing
language:en
Short-container-title:Canadian Journal of Remote Sensing

Author:

Júnior Clóvis Cechim¹^ORCID,Shemmer Rosangela Carline¹^ORCID,Johann Jerry Adriani¹^ORCID,de Almeida Pereira Gabriel Henrique²^ORCID,Deppe Flávio²^ORCID,Opazo Miguel Angel Uribe¹^ORCID,Silva Junior Carlos Antonio da³^ORCID

Affiliation:

1. Agricultural Engineering Department, State University of West Paraná (UNIOESTE), Cascavel, Brazil;

2. Meteorological System of Paraná (SIMEPAR), Curitiba, Brazil;

3. Department of Geography, State University of Mato Grosso (UNEMAT), Sinop, Brazil

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/07038992.2019.1594734

Reference42 articles.

1. Comparative Analysis of MODIS Time-Series Classification Using Support Vector Machines and Methods Based upon Distance and Similarity Measures in the Brazilian Cerrado-Caatinga Boundary

2. Antunes, J.F.G., Lamparelli, R.A.C., Rodrigues, L.H.A. 2015. “Mapeamento do cultivo da cana-de-açúcar por meio da classificação de séries temporais de dados MODIS.” In XVII Simpósio Brasileiro de Sensoriamento Remoto, pp. 3237–3244. João Pessoa. Anais… João Pessoa.

3. Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

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