Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification

This book covers the state of art image classification methods for discrimination of earth objects from remote sensing satellite data with an emphasis on fuzzy based learning methods including applications for preparing land cover classification outputs from actual satellite data. It provides details about the temporal indices database using proposed class-based sensor independent approach supported by practical examples. Fuzzy based algorithms with machine learning algorithms to prepare land cover maps is discussed.

Accuracy assessment for soft classification outputs are included and all algorithms are supported by in-house developed tool as Sub-pixel Multi-spectral Image Classifier. Aimed at researchers, graduate students, professionals in earth remote sensing, remote sensing image and data processing, geography and geoinformation science, image classification, this book:Exclusively focuses on using fuzzy classification to remote sensing images. Covers Sub Pixel Multi-Spectral Image Classifier Tool (SMIC) to support discussed algorithms.

Explains fuzzy and learning based classifiers with in-house developed SMIC tool. Discusses how ANN, CNN, RNN and hybrid learning classifiers application on remote sensing images. Combines explanation of the algorithms with examples, graph, and charts.