Remotely sensed soil data analysis using artificial neural
networks. A case study of El-Fayoum depression, Egypt

k 2014

Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt

AMATO, Filippo, Josef HAVEL, Abd-Alla GAD a Ahmed EL-ZEINY

Základní údaje

Originální název

Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt

Autoři

AMATO, Filippo (380 Itálie, domácí), Josef HAVEL (203 Česká republika, garant, domácí), Abd-Alla GAD (818 Egypt) a Ahmed EL-ZEINY (818 Egypt)

Vydání

ICRISSM 2014, 2014

Další údaje

Jazyk

angličtina

Typ výsledku

Prezentace na konferencích

Obor

10406 Analytical chemistry

Stát vydavatele

Egypt

Utajení

není předmětem státního či obchodního tajemství

Kód RIV

RIV/00216224:14310/14:00078217

Organizační jednotka

Přírodovědecká fakulta

Klíčová slova anglicky

remote sensing; satellite data; artificial neural networks

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 12. 1. 2015 17:24, Mgr. Filippo Amato, Ph.D.

Anotace

V originále

Earth observation and monitoring of soil quality, long term changes of soil characteristics and deterioration processes such as degradation or desertification are among the most important objectives of remote sensing. The georeferenciation of such information contribute to the development and progress of Digital Earth project in the framework of information globalization process. Earth observation and soil quality monitoring via remote sensing are mostly based on the use of satellite spectral data. Advanced techniques are available to predict the soil or land use/cover categories from satellite imagery data. Artificial Neural Networks (ANNs) are among the most widely used tools for modeling and prediction purposes in various field of science. The assessment of satellite images quality and suitability for analysing the soil conditions (e.g., soil classification, land use/cover estimation, etc.) is fundamental. In this work, methodology for the preliminary data exploration and subsequent application of ANNs in remote sensing is presented. It consists of preliminary explorative data analysis and of ANNs application. The first stage is achieved via: (i) elimination of outliers, (ii) data pre-processing and (iii) the determination of the number of distinguishable soil “classes” via Eigenvalues Analysis (EA) and Principal Components Analysis (PCA). The next stage of ANNs use consists of: (i) building the training database, (ii) optimization of ANN architecture and database cleaning and (iii) training and verification of the network. Application of the proposed methodology will be given.

Návaznosti

MSM0021622411, záměr

Název: Studium a aplikace plazmochemických reakcí v neizotermickém nízkoteplotním plazmatu a jeho interakcí s povrchem pevných látek

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Studium a aplikace plazmochemických reakcí v neizotermickém nízkoteplotním plazmatu a jeho interakcí s povrchem pevných látek

Citovat

AMATO, Filippo, Josef HAVEL, Abd-Alla GAD a Ahmed EL-ZEINY. Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt. In ICRISSM 2014. 2014.

@proceedings{1216158,
   author = {Amato, Filippo and Havel, Josef and Gad, AbdandAlla and ElandZeiny, Ahmed},
   booktitle = {ICRISSM 2014},
   keywords = {remote sensing; satellite data; artificial neural networks},
   language = {eng},
   title = {Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt},
   year = {2014}
}

TY  - CONF
ID  - 1216158
AU  - Amato, Filippo - Havel, Josef - Gad, Abd-Alla - El-Zeiny, Ahmed
PY  - 2014
TI  - Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt
KW  - remote sensing
KW  - satellite data
KW  - artificial neural networks
N2  - Earth observation and monitoring of soil quality, long term changes of soil characteristics and deterioration processes such as degradation or desertification are among the most important objectives of remote sensing. The georeferenciation of such information contribute to the development and progress of Digital Earth project in the framework of information globalization process. Earth observation and soil quality monitoring via remote sensing are mostly based on the use of satellite spectral data. Advanced techniques are available to predict the soil or land use/cover categories from satellite imagery data. Artificial Neural Networks (ANNs) are among the most widely used tools for modeling and prediction purposes in various field of science. The assessment of satellite images quality and suitability for analysing the soil conditions (e.g., soil classification, land use/cover estimation, etc.) is fundamental. In this work, methodology for the preliminary data exploration and subsequent application of ANNs in remote sensing is presented. It consists of preliminary explorative data analysis and of ANNs application. The first stage is achieved via: (i) elimination of outliers, (ii) data pre-processing and (iii) the determination of the number of distinguishable soil “classes” via Eigenvalues Analysis (EA) and Principal Components Analysis (PCA). The next stage of ANNs use consists of: (i) building the training database, (ii) optimization of ANN architecture and database cleaning and (iii) training and verification of the network. Application of the proposed methodology will be given.
ER  -

AMATO, Filippo, Josef HAVEL, Abd-Alla GAD a Ahmed EL-ZEINY. Remotely sensed soil data analysis using artificial neural networks. A case study of El-Fayoum depression, Egypt. In \textit{ICRISSM 2014}. 2014.

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