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LAND COVER CLASSIFICATION BASED ON MODIS IMAGERY DATA USING ARTIFICIAL NEURAL NETWORKS

Authors: Stepchenko, Arthur;

LAND COVER CLASSIFICATION BASED ON MODIS IMAGERY DATA USING ARTIFICIAL NEURAL NETWORKS

Abstract

Remote sensing has been widely used to obtain land cover information using automated classification. Land cover is a measure of what is overlaying the surface of the earth. Accurate mapping of land cover on a regional scale is useful in such fields as precision agriculture or forest management and is one of the most important applications in remote sensing. In this study, multispectral MODIS Terra NDVI images and an artificial neural network (ANN) were used in land cover classification. Artificial neural network is a computing tool that is designed to simulate the way the human brain analyzes and process information. Artificial neural networks are one of the commonly applied machine learning algorithm, and they have become popular in the analysis of remotely sensed data, particularly in classification or feature extraction from image data more accurately than conventional method. This paper focuses on an automated classification system based on a pattern recognition neural network. Variational mode decomposition method is used as an image data pre-processing tool in this classification system. The result of this study will be land cover map.

Country
Latvia
Subjects by Vocabulary

Microsoft Academic Graph classification: Artificial neural network Multispectral image Feature extraction Land cover computer.software_genre Normalized Difference Vegetation Index Geography Pattern recognition (psychology) Data mining Precision agriculture Scale (map) computer

Keywords

Pattern Recognition, Normalized Difference Vegetation Index, Artificial Neural Networks; Normalized Difference Vegetation Index; Pattern Recognition; Variational Mode Decomposition, Variational Mode Decomposition, Artificial Neural Networks

12 references, page 1 of 2

[1] L. Zhou and X. Yang, "Use of neural networks for land cover classification from remotely sensed imagery," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII, part B7, Beijing 2008.

[2] M. K. Arora, "Land cover classification from remote sensing data," GIS@development, vol. 6, no. 3, pp. 24-25, 30-31, 2002.

[3] K. Jia , S. Liang, X. Wei, Y. Yao, Y. Su, B. Jiang and X. Wang, "Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data," Remote Sens., vol. 6, pp. 11518-11532, 2014. [OpenAIRE]

[4] N. B. Duy and T. T. H. Giang, "Study on vegetation indices selection and changing detection thresholds selection in Land cover change detection assessment using change vector analysis," presented at International Environmental Modelling and Software Society (iEMSs), Sixth Biennial Meeting, Leipzig, Germany, 2012.

[5] E. Sahebjalal and K. Dashtekian, "Analysis of land use-land covers changes using normalized difference vegetation index (NDVI) differencing and classification methods," African Journal of Agricultural Research, vol. 8, no. 37, pp. 4614- 4622, September 26, 2013. [OpenAIRE]

[6] A. Mert, "ECG feature extraction based on the bandwidth properties of variational mode decomposition," Physiol Meas., vol. 37, no. 4, pp. 530-543, April 2016.

[7] T. J. Cleophas and A. H. Zwinderman, Statistics Applied to Clinical Studies. Springer Netherlands: 2012. ISBN: 978-94- 007-2862-2.

[8] C. Aneesh, S. Kumar, P. M. Hisham and K. P. Soman, "Performance Comparison of Variational Mode Decomposition over Empirical Wavelet Transform for the Classification of Power Quality Disturbances Using Support Vector Machine," Procedia Computer Science, vol. 46, pp. 372-380, 2015. [OpenAIRE]

[9] S. Liu, G. Tang, X. Wang and Y. He, "Time-Frequency Analysis Based on Improved Variational Mode Decomposition and Teager Energy Operator for Rotor System Fault Diagnosis," Mathematical Problems in Engineering, vol. 2016, article ID 1713046, 2016.

[10] A. Shabri and R. Samsudin, ''Daily crude oil price forecasting using hybridizing wavelet and artificial neural network model,'' Mathematical Problems in Engineering, vol. 2014, article ID 201402, 2014. [OpenAIRE]

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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