Deep learning for hyperspectral image analysis and classification This book focuses on deep learning based methods for hyperspectral image (HSI) analysis. Unsupervised spectral spatial adaptive band noise factor based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review which also highlights some potentialities and critical issues related to the observed development trends. Introduction Hyperspectral Imaging System Classification Techniques for HSI Preprocessing: Noise Reduction Band Categorization for HSI Spatial Feature Extraction Using Segmentation Multiple Deep learning models for feature extraction in classification Deep learning for merging spatial and spectral information in classification Sparse cording for Hyperspectral Data Classification Applications of HSI classification Conclusion.