Classification Methods for Remotely Sensed DataCRC Press, 19 apr 2016 - 376 pagine Since the publishing of the first edition of Classification Methods for Remotely Sensed Data in 2001, the field of pattern recognition has expanded in many new directions that make use of new technologies to capture data and more powerful computers to mine and process it. What seemed visionary but a decade ago is now being put to use and refined in |
Sommario
| 1 | |
Chapter 2 Pattern Recognition Principles | 41 |
Chapter 3 Artificial Neural Networks | 77 |
Chapter 4 Support Vector Machines | 125 |
Chapter 5 Methods Based on Fuzzy Set Theory | 155 |
Chapter 6 Decision Trees | 183 |
Chapter 7 Texture Quantization | 221 |
Chapter 8 Modeling Context Using Markov Random Fields | 255 |
Chapter 9 Multisource Classification | 283 |
| 317 | |
| 349 | |
Back cover | 357 |
Parole e frasi comuni
accuracy algorithm angle applied approach attribute band calculated called Chapter classification cluster combination computed concept considered contains corresponding decision tree defined denotes derived described determined dimension direction distance distribution effect energy Equation error estimate et al example expressed feature space field filter function further fuzzy gain given Greenness High illustrated increases input instance iterations label layer learning mapping matrix mean measure membership methods neurone node noise normally Note object obtained output parameters pattern performance pixel present probability problem procedure pruning radar random range reflectance relationship remote sensing represented resolution respectively rule samples selected sensor separate shown in Figure shows spatial specific split statistical step subset surface SVMs Table texture tion training data transform vector wavelet weights