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 machine learning and deep learning algorithms. Both types of algorithms are described in such details that these can be implemented directly for thematic mapping of multiple-class or specific-class landcover from multispectral optical remote sensing data. These algorithms along with multi-date multi-sensor remote sensing are capable to monitor specific stage (for e. g. phenology of growing crop) of a particular class also included. With these capabilities fuzzy machine learning algorithms have strong applications in areas like crop insurance forest fire mapping stubble burning post disaster damage mapping etc. It also provides details about the temporal indices database using proposed Class Based Sensor Independent (CBSI) approach supported by practical examples. As well this book addresses other related algorithms based on distance kernel based as well as spatial information through Markov Random Field (MRF)/Local convolution methods to handle mixed pixels non-linearity and noisy pixels. Further this book covers about techniques for quantiative assessment of soft classified fraction outputs from soft classification and supported by in-house developed tool called sub-pixel multi-spectral image classifier (SMIC). It is aimed at graduate postgraduate research scholars and working professionals of different branches such as Geoinformation sciences Geography Electrical Electronics and Computer Sciences etc. working in the fields of earth observation and satellite image processing. Learning algorithms discussed in this book may also be useful in other related fields for example in medical imaging. Overall this book aims to: exclusive focus on using large range of fuzzy classification algorithms for remote sensing images; discuss ANN CNN RNN and hybrid learning classifiers application on remote sensing images; describe sub-pixel multi-spectral image classifier tool (SMIC) to support discussed fuzzy and learning algorithms; explain how to assess soft classified outputs as fraction images using fuzzy error matrix (FERM) and its advance versions with FERM tool Entropy Correlation Coefficient Root Mean Square Error and Receiver Operating Characteristic (ROC) methods and; combines explanation of the algorithms with case studies and practical applications. | Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification