Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification (Hardcover, 1)

I. Machine Learning 1.Introduction2.Pattern Recognition3.Machine Learning Algorithms for Pattern Recognition II.Ground Truth Data for Remote Sensing Image Classification1.Criteria for Ground Truth Data2.Training Data3.Testing Data III.Fuzzy Classifiers1.Soft Classifiers2.Traditional Classifiers vs Soft Classifiers3.Linear and non-linear classifiers4.Fuzzy c-Means (FCM) Classifier5.Possibilistic c-Means (PCM) Classifier6.Noise Clustering (NC) Classifier7.Why Noise Clustering?8.Limitations of Possibilistic c-Means (PCM)9.Improved Possibilistic c-Means (IPCM)10.Advantages of IPCM over PCM11.Modified Possibilistic c-Means (MPCM) IV.Learning Based Classifiers1.Artificial Neural Network (ANN)2.Convolutional Neural Network (CNN)3.Recurrent Neural Network (RNN)4.Hybrid Learning Network (HLN)5.Deep Learning Concepts6.In-house Tool for Study of Learning Algorithms V.Hybrid Fuzzy Classifiers1.Entropy Based Hybrid Soft Classifiers 2.Fuzzy c-Means with Entropy (FCME)3.Noise Clustering with Entropy (NCWE) Classifier4.Similarity/Dissimilarity Measures in Fuzzy Classifiers5.Kernels Concept in Fuzzy Classifiers 6.Theory behind Markov Random Field (MRF)7.Types of MRF methods8.Contextual Information using MRF9.Convolution based Local Information in Fuzzy Classifiers VI.Fuzzy Classifiers for Temporal Data Processing 1.Introduction2.Indices Approaches3.Fuzzy Based Algorithms for Single Class Extraction4.Concept for Mono/Bi-sensor Remote Sensing Data Processing VII.Assessment of Accuracy for Soft Classification1.Generation of Testing Data2.Methods for Assessment of Accuracy3.Fuzzy Error Matrix (FERM) and Other Operators4.Entropy Method5.Mean and Variance Method for Edge Preservation6.Correlation Coefficient7.Root Mean Square Error8.Receiver Operating Characteristics (ROC) Appendix A1SMIC: Sub_Pixel Multi-spectral Image Classifier Tool Appendix A2Case Study 1 : Study of similarity and dissimilarity measures with IPCM and MPCM classifiersCase Study 2 : Bi-sensor temporal data for paddy crop mappingCase Study 3 : Handling non-linearity between classes using kernels in fuzzy classifiersCase Study 4 : Handling noise through MRF based noise clustering classifierCase Study 5 :Local convolution based contextual information in Possibilistic c-Means ClassificationCase Study 6 : Optimization of local convolution based MPCM classifier and identification of paddy and burnt paddy fields Case Study 7 : Semi-supervised training approach for PCM classifierCase Study 8 : Study of hybridizing stochastic and deterministic measures with fuzzy based classifier Case Study 9 : Kernal based PCM Classification approachCase Study 10 : Effect of red edge bands in fuzzy classification: a case study of sunflower cropCase Study 11 : Discriminating sugar ratoon / plant crop using multi- sensor temporal data