TY - BOOK ID - 81148464 TI - Deep Learning for Hyperspectral Image Analysis and Classification AU - Tao, Linmi AU - Mughees, Atif PY - 2021 SN - 9813344202 9813344199 PB - Singapore : Springer Nature Singapore : Imprint: Springer, DB - UniCat KW - Image processing KW - Digital techniques. KW - Digital image processing KW - Digital electronics KW - Machine learning. KW - Artificial intelligence. KW - Computer vision. KW - Signal processing. KW - Machine Learning. KW - Artificial Intelligence. KW - Computer Imaging, Vision, Pattern Recognition and Graphics. KW - Computer Vision. KW - Signal, Speech and Image Processing . KW - Processing, Signal KW - Information measurement KW - Signal theory (Telecommunication) KW - Machine vision KW - Vision, Computer KW - Artificial intelligence KW - Pattern recognition systems KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Learning, Machine UR - https://www.unicat.be/uniCat?func=search&query=sysid:81148464 AB - 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. ER -