Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network

Erwin Dianderas; Kevin Rojas; Guillermo Kemper

doi:10.1109/STSIVA.2014.7010132

Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network

Erwin Dianderas
, Kevin Rojas
, Guillermo Kemper

Technologies and Communications
Universidad Nacional de Ingeniería, Peru

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In this article is developed a technique that allows to calculate the presence of vegetation, glaciers and water bodies through multispectral image processing employing a Multi-layer Perceptron Neural Netwok, giving the option to discriminate the presence of lakes to generate the cadastral registration of these. The supervised classification that was implemented has a high level of robustness and reliability, since the validation of the data obtained at a geolocation level have a 0% of error and the parameters of the area and perimeter an approximate error of 10%.

Original language	English
Title of host publication	2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479976669
DOIs	https://doi.org/10.1109/STSIVA.2014.7010132
State	Published - 14 Jan 2015
Externally published	Yes
Event	2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014 - Armenia-Quindio, Colombia Duration: 17 Sep 2014 → 19 Sep 2014

Publication series

Name	2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014

Conference

Conference	2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014
Country/Territory	Colombia
City	Armenia-Quindio
Period	17/09/14 → 19/09/14

Keywords

Geolocalisation
Landsat 8
Neural Network(NN)
Normal Difference Snow Index (NDSI)
Normal Difference Vegetation Index (NDVI)
Normal Difference Water Index (NDWI)
Satellite Image Processing

Access to Document

10.1109/STSIVA.2014.7010132

Cite this

Dianderas, E., Rojas, K., & Kemper, G. (2015). Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network. In 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014 Article 7010132 (2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/STSIVA.2014.7010132

Dianderas, Erwin ; Rojas, Kevin ; Kemper, Guillermo. / Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network. 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014. Institute of Electrical and Electronics Engineers Inc., 2015. (2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014).

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title = "Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network",

abstract = "In this article is developed a technique that allows to calculate the presence of vegetation, glaciers and water bodies through multispectral image processing employing a Multi-layer Perceptron Neural Netwok, giving the option to discriminate the presence of lakes to generate the cadastral registration of these. The supervised classification that was implemented has a high level of robustness and reliability, since the validation of the data obtained at a geolocation level have a 0\% of error and the parameters of the area and perimeter an approximate error of 10\%.",

keywords = "Geolocalisation, Landsat 8, Neural Network(NN), Normal Difference Snow Index (NDSI), Normal Difference Vegetation Index (NDVI), Normal Difference Water Index (NDWI), Satellite Image Processing",

author = "Erwin Dianderas and Kevin Rojas and Guillermo Kemper",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014 ; Conference date: 17-09-2014 Through 19-09-2014",

year = "2015",

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doi = "10.1109/STSIVA.2014.7010132",

language = "Ingl{\'e}s",

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Dianderas, E, Rojas, K & Kemper, G 2015, Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network. in 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014., 7010132, 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014, Institute of Electrical and Electronics Engineers Inc., 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014, Armenia-Quindio, Colombia, 17/09/14. https://doi.org/10.1109/STSIVA.2014.7010132

Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network. / Dianderas, Erwin; Rojas, Kevin; Kemper, Guillermo.
2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014. Institute of Electrical and Electronics Engineers Inc., 2015. 7010132 (2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network

AU - Dianderas, Erwin

AU - Rojas, Kevin

AU - Kemper, Guillermo

PY - 2015/1/14

Y1 - 2015/1/14

N2 - In this article is developed a technique that allows to calculate the presence of vegetation, glaciers and water bodies through multispectral image processing employing a Multi-layer Perceptron Neural Netwok, giving the option to discriminate the presence of lakes to generate the cadastral registration of these. The supervised classification that was implemented has a high level of robustness and reliability, since the validation of the data obtained at a geolocation level have a 0% of error and the parameters of the area and perimeter an approximate error of 10%.

AB - In this article is developed a technique that allows to calculate the presence of vegetation, glaciers and water bodies through multispectral image processing employing a Multi-layer Perceptron Neural Netwok, giving the option to discriminate the presence of lakes to generate the cadastral registration of these. The supervised classification that was implemented has a high level of robustness and reliability, since the validation of the data obtained at a geolocation level have a 0% of error and the parameters of the area and perimeter an approximate error of 10%.

KW - Geolocalisation

KW - Landsat 8

KW - Neural Network(NN)

KW - Normal Difference Snow Index (NDSI)

KW - Normal Difference Vegetation Index (NDVI)

KW - Normal Difference Water Index (NDWI)

KW - Satellite Image Processing

UR - https://www.scopus.com/pages/publications/84922496382

U2 - 10.1109/STSIVA.2014.7010132

DO - 10.1109/STSIVA.2014.7010132

M3 - Contribución a la conferencia

AN - SCOPUS:84922496382

T3 - 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014

BT - 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014

Y2 - 17 September 2014 through 19 September 2014

ER -

Dianderas E, Rojas K, Kemper G. Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network. In 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014. Institute of Electrical and Electronics Engineers Inc. 2015. 7010132. (2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014). doi: 10.1109/STSIVA.2014.7010132

Identification and cadastral registration of water bodies through multispectral image processing with multi-layer Perceptron Neural Network

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Keywords

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