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

被引：6

作者：

Cechim Junior, Clovis ^{[1
]}

Shemmer, Rosangela Canine ^{[1
]}

Johann, Jerry Adriani ^{[1
]}

de Almeida Pereira, Gabriel Henrique ^{[2
]}

Deppe, Flavio ^{[2
]}

Uribe Opazoa, Miguel Angel ^{[1
]}

da Silva Junior, Carlos Antonio ^{[3
]}

机构：

[1] State Univ West Parana UNIOESTE, Agr Engn Dept, Cascavel, Brazil

[2] Meteorol Syst Parana SIMEPAR, Curitiba, Parana, Brazil

[3] State Univ Mato Grosso UNEMAT, Dept Geog, Sinop, Brazil

来源：

CANADIAN JOURNAL OF REMOTE SENSING | 2019年 / 45卷 / 01期

关键词：

PRINCIPAL COMPONENT ANALYSIS; TIME-SERIES; VEGETATION INDEXES; CLASSIFICATION; PARANA; STATE; IMAGES; BRAZIL; AREAS; YIELD;

D O I：

10.1080/07038992.2019.1594734

中图分类号：

TP7 [遥感技术];

学科分类号：

081102 ; 0816 ; 081602 ; 083002 ; 1404 ;

摘要：

The objective of this research was to distinguish and estimate cultivated areas of soybean and corn in Parana State, Brazil, in the 2014/2015 crop season. The main obstacle in mapping summer crops using vegetation index images is to separate the cultivated areas with soybean and corn. These crops planted in a similar period present similar spectral signatures. Thus, with the use of Data Mining techniques (DM) and Artificial Neural Network (ANN) it was possible to carry out the crop mapping, even for those that present similarities in spectral-temporal profile of vegetation indexes. The accuracy obtained in the mappings resulted in a KI (Kappa Index) of 0.78 and 89% of OA (overall accuracy) indicating a high accuracy in the separation of soybean and corn summer crops.

引用

页码：16 / 25

页数：10

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