Computer Processing Of Remotely Sensed Images An Introduction

By L. Vogler. 2016

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Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, methodology): “The commonly adopted empirical statistical methods for high-throughput field phenotyping include multiple linear regression, partial least squares regression and stepwise linear regression (Richards, 1990).”
Implementation of machine-learning classification in remote sensing: an applied review (2018, methodology): “In contrast, feature selection methods select a subset of the original variables that are determined to be valuable for the classi ﬁ cation (Khalid, Khalil, and Nasreen 2014; Liu and Motoda 1998; Mather 2004; Pal and Foody 2010).”
Big Data for Remote Sensing: Challenges and Opportunities (2016, methodology): “Remote sensing data are preprocessed by geometric and radiometric correction, georeferen-cing, noise removal, etc. [18], and the data from cyber-space should be cleaned to reduce errors and noise, in which data quality can be improved.”