Soil texture classification with artificial neural networks operating on remote sensing data

2006 ◽  
Vol 54 (2) ◽  
pp. 53-68 ◽  
Author(s):  
Yushun Zhai ◽  
J. Alex Thomasson ◽  
Julian E. Boggess ◽  
Ruixiu Sui
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Soil Texture ◽  
Texture Classification ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Artificial Neural

Artificial neural networks paddy-field classifier using spatiotemporal remote sensing data

2010 ◽  
Vol 15 (2) ◽  
pp. 221-224 ◽  
Author(s):  
Takashi Yamaguchi ◽  
Kazuya Kishida ◽  
Eiji Nunohiro ◽  
Jong Geol Park ◽  
Kenneth J. Mackin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Paddy Field ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Artificial Neural

scholarly journals Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

2013 ◽  
Vol 37 (2) ◽  
pp. 339-351 ◽  
Author(s):  
César da Silva Chagas ◽  
Carlos Antônio Oliveira Vieira ◽  
Elpídio Inácio Fernandes Filho
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Maximum Likelihood ◽  
Remote Sensing Data ◽  
Soil Mapping ◽  
Slope Aspect ◽  
Sensing Data ◽  
Terrain Attributes ◽  
Artificial Neural

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

Determination of application volume for coffee plantations using artificial neural networks and remote sensing

2021 ◽  
Vol 184 ◽  
pp. 106096
Author(s):  
Mailson Freire de Oliveira ◽  
Adão Felipe dos Santos ◽  
Elizabeth Haruna Kazama ◽  
Glauco de Souza Rolim ◽  
Rouverson Pereira da Silva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coffee Plantations ◽  
Artificial Neural ◽  
Application Volume
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