ABSTRACT
CONTENTS 11.1 Introduction ..................................................................................................................... 250 11.2 The ‘‘Multi’’ Concept in Remote Sensing ................................................................... 250
11.2.1 The Multi-Spectral or Multi-Frequency Aspect ........................................... 250 11.2.2 The Multi-Temporal Aspect............................................................................ 251 11.2.3 The Multi-Polarization Aspect........................................................................ 251 11.2.4 The Multi-Sensor Aspect ................................................................................. 251 11.2.5 Other Sources of Spatial Data ......................................................................... 251
11.3 Multi-Sensor Data Registration .................................................................................... 252 11.4 Multi-Sensor Image Classification ............................................................................... 254
11.4.1 A General Introduction to Multi-Sensor Data Fusion for Remote-Sensing Applications................................................................... 254
11.4.2 Decision-Level Data Fusion for Remote-Sensing Applications ................ 254 11.4.3 Combination Schemes for Combining Classifier Outputs......................... 256 11.4.4 Statistical Multi-Source Classification ........................................................... 257 11.4.5 Neural Nets for Multi-Source Classification ................................................ 257 11.4.6 A Closer Look at Dempster-Shafer Evidence Theory for Data Fusion... 258 11.4.7 Contextual Methods for Data Fusion ............................................................ 259 11.4.8 Using Markov Random Fields to Incorporate Ancillary Data .................. 260 11.4.9 A Summary of Data Fusion Architectures ................................................... 260
11.5 Multi-Temporal Image Classification.......................................................................... 260 11.5.1 Multi-Temporal Classifiers.............................................................................. 263
11.5.1.1 Direct Multi-Date Classification .................................................... 263 11.5.1.2 Cascade Classifiers .......................................................................... 263 11.5.1.3 Markov Chain and Markov Random Field Classifiers.............. 264 11.5.1.4 Approaches Based on Characterizing the Temporal
Signature............................................................................................ 264 11.5.1.5 Other Decision-Level Approaches to Multi-Temporal
Classification..................................................................................... 264 11.6 Multi-Scale Image Classification .................................................................................. 264 11.7 Concluding Remarks...................................................................................................... 266
11.7.1 Fusion Level....................................................................................................... 267 11.7.2 Selecting a Multi-Sensor Classifier................................................................. 267 11.7.3 Selecting a Multi-Temporal Classifier ........................................................... 267 11.7.4 Approaches for Multi-Scale Data ................................................................... 267
Acknowledgment....................................................................................................................... 267 References ................................................................................................................................... 267
Earth observation is currently developing more rapidly than ever before. During the last decade the number of satellites has been growing steadily, and the coverage of the Earth in space, time, and the electromagnetic spectrum is increasing correspondingly fast.
