The fifth edition of this core textbook in advanced remote sensing maintains the same theoretical material with necessary updates. The software tools have evolved substantially, and the fifth edition replaces Python 2 with Python 3 and uses the high-level packages based on it, such as Colab, Pytorch, KERAS, Scikit-Learn.
The fifth edition of this core textbook in advanced remote sensing maintains the same theoretical material with necessary updates. The software tools have evolved substantially, and the fifth edition replaces Python 2 with Python 3 and uses the high-level packages based on it, such as Colab, Pytorch, KERAS, Scikit-Learn.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Morton John Canty was a senior research scientist in the Institute for Bio- and Geosciences at the Juelich Research Center in Germany and is now retired. He received his PhD in Nuclear Physics in 1969 at the University of Manitoba, Canada. He has served on numerous advisory bodies for the German federal government and Vienna's International Atomic Energy Agency. He was also a coordinator within the European Network of Excellence on Global Monitoring for Security and Stability, funded by the European Commission. Morton Canty is the author of three monographs in the German language: about non-linear dynamics, neural networks for classification of remote sensing data, and algorithmic game theory. The latter text has appeared in a revised English version (Resolving Conflicts with Mathematica published in 2003). He also co-authored a monograph on mathematical methods for treaty verification (Compliance Quantified published in 1996). He has published many papers about experimental nuclear physics, nuclear safeguards, applied game theory, and remote sensing. He has lectured on nonlinear dynamical growth models and remote sensing digital image analysis to graduate and undergraduate students at Universities in Bonn, Berlin, Freiberg/Saxony, and Rome.
Inhaltsangabe
1. Images, Arrays, and Matrices. 2. Image Statistics. 3. Transformations. 4. Filters, Kernels, and Fields. 5. Image Enhancement and Correction. 6. Supervised Classification Part 1. 7. Supervised Classification Part 2. 8. Unsupervised Classification. 9. Change Detection. Appendix A: Mathematical Tools. Appendix B: Neural Network Training Algorithms. Appendix C: Software.
