Feature Engineering of Remote Sensing Satellite Imagery Using Principal Component Analysis for Efficient Crop Yield Prediction

Divakar, M. Sarith; Elayidom, M. Sudheep; Rajesh, R.

doi:10.1007/978-981-16-9605-3_13

Cited by 1 publication

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“…Boosting is a method used in machine learning to reduce errors in predictive data analysis [52] . Data scientists train machine learning software, called machine learning models, on labelled data to make guesses about unlabeled data [53] .…”

Section: Boostingmentioning

confidence: 99%

Machine learning application in GIS and remote sensing: An overview

Upreti¹

2022

IJMRGE

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Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing based projects.

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Section: Boostingmentioning

confidence: 99%

Machine learning application in GIS and remote sensing: An overview

Upreti¹

2022

IJMRGE

View full text Add to dashboard Cite

show abstract

Feature Engineering of Remote Sensing Satellite Imagery Using Principal Component Analysis for Efficient Crop Yield Prediction

Cited by 1 publication

References 9 publications

Machine learning application in GIS and remote sensing: An overview

Machine learning application in GIS and remote sensing: An overview

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