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LAND COVER CLASSIFICATION BASED ON MODIS IMAGERY DATA USING ARTIFICIAL NEURAL NETWORKS
Copyright policy )LAND COVER CLASSIFICATION BASED ON MODIS IMAGERY DATA USING ARTIFICIAL NEURAL NETWORKS
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.
- Ventspils University College Latvia
- Riga Technical University Latvia
Microsoft Academic Graph classification: Artificial neural network Multispectral image Feature extraction Land cover computer.software_genre Normalized Difference Vegetation Index Geography Pattern recognition (psychology) Precision agriculture Data mining Scale (map) computer
Pattern Recognition, Normalized Difference Vegetation Index, Artificial Neural Networks; Normalized Difference Vegetation Index; Pattern Recognition; Variational Mode Decomposition, Variational Mode Decomposition, Artificial Neural Networks
Pattern Recognition, Normalized Difference Vegetation Index, Artificial Neural Networks; Normalized Difference Vegetation Index; Pattern Recognition; Variational Mode Decomposition, Variational Mode Decomposition, Artificial Neural Networks
Microsoft Academic Graph classification: Artificial neural network Multispectral image Feature extraction Land cover computer.software_genre Normalized Difference Vegetation Index Geography Pattern recognition (psychology) Precision agriculture Data mining Scale (map) computer
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[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.
[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.
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[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.
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Citations provided by BIP!Popularity provided by BIP!- Ventspils University College Latvia
- Riga Technical University Latvia
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.