Machine Learning Algorithms for Optical Remote Sensing Data Classification and Analysis

Abstract

In recent years, Machine Learning (ML) algorithms have gained much attention and found a profound importance in processing, classification as well as analysis of multispectral, and hyperspectral remotely sensed data. The core objectives of this chapter are firstly to provide a critical review on important advanced ML algorithms in remote sensing data classification, and analysis; secondly, examine the performance of widely used important supervised ML algorithms namely Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART) in satellite image classification, and analysis on Google Earth Engine (GEE) platform to derive distinct Land Use/Land Cover (LULC) classes. ML algorithms are being extensively used in optical remote sensing data analysis it includes the image classification algorithms to precisely allocate objects to a distinct set of known classes, the clustering algorithms to group the objects into classes based on a given set of input variables, the regression algorithms to forecast a response variable from a given a set of covariates, and the dimensionality reduction algorithms to build a small set of new variables that includes most of the information available in the input set of numerous variables. In the study, among the three tested supervised ML algorithms in LULC classification, CART algorithm shows relatively better performance than the RF, and SVM algorithms. The study concludes that advanced ML algorithms have immense potential in optical remote sensing data classification, and analysis to attain the higher classification accuracy.