Remote Sensing Image Analysis

Cover -- Contents -- Preface -- Contents -- Acknowledgements -- 1 Basics of Remote Sensing -- 1.1 Introduction -- 1.2 Historic overview -- 1.3 Concepts of Remote Sensing -- 1.3.1 Sources of electromagnetic radiation -- 1.3.2 The atmosphere -- 1.3.3 Object -- radiation interaction -- 1.3.4 Sensors -- 1.3.5 Transmission, reception and (pre- )processing -- 1.3.6 Image analysis and interpretation -- 1.3.7 The final product -- 1.4 The chapters in this book -- 2 Spatial Variability, Mapping Methods, Image Analysis and Pixels -- 2.1 Introduction -- 2.2 Spatial variability in landscapes -- 2.2.1 Spatial variation -- 2.2.2 Hierarchical mapping approaches -- 2.2.3 Spectral properties of vegetation -- 2.3 Registration of spatial information by Remote Sensing sensors -- 2.4 Statistical models for Remote Sensing data -- 2.5 The spatial and spectral classifier -- 2.6 Conclusions -- 3 Sub-Pixel Methods in Remote Sensing -- 3.1 Introduction -- 3.2 Mixed pixels -- 3.3 Estimating sub-pixel class composition -- 3.3.1 Spectral unmixing -- 3.3.2 Soft classification -- 3.4 Current research topics -- 3.5 Conclusions -- 4 Resolution Manipulation and Sub-Pixel Mapping -- 4.1 Introduction -- 4.2 Scale in Remote Sensing -- 4.2.1 Defining scale -- 4.2.2 Scales of measurement -- 4.2.3 Scales of spatial variation -- 4.3 Geostatistics -- 4.3.1 The RF model -- 4.3.2 Variogram estimation and modelling -- 4.3.3 Interpreting the modelled variogram -- 4.3.4 Regularizing the variogram model -- 4.3.5 Kriging -- 4.4 Changing the scale of measurement -- 4.4.1 Upscaling -- 4.4.2 Downscaling -- 4.5 Discussion -- 4.5.1 Applications -- 4.5.2 Issues of generalization and accuracy -- 4.5.3 Error and the PSF -- 4.5.4 Future research -- 4.6 Conclusion -- 5 Multiscale Object-Specific Analysis (MOSA): An Integrative Approach for Multiscale Landscape Analysis -- 5.1 Introduction -- 5.2 Methodology -- 5.2.1 Remote Sensing dataset -- 5.2.2 MOSA description -- 5.3 Results -- 5.4 Discussion -- 5.5 Conclusion -- 6 Variogram Derived Image Texture for Classifying Remotely Sensed Images -- 6.1 Introduction -- 6.2 Texture and variogram -- 6.2.1 The variogram function as a textural descriptor -- 6.2.2 Geostatistical texture operators based on the variogram -- 6.3 Calculating geostatistical texture images -- 6.3.1 Using variogram-based texture operators -- 6.3.2 Using the cross validation method -- 6.4 Using the variogram function in Remotely Sensed image classification -- 6.4.1 An example of using geostatistical texture images in supervised digital classification -- 6.5 Conclusions -- 7 Merging Spectral and Textural Information for Classifying Remotely Sensed Images -- 7.1 Introduction -- 7.2 Background -- 7.3 Texture measures -- 7.4 Per-pixel vs per-field approach to image classification -- 7.5 Classification algorithms -- 7.6 Study area -- 7.7 Image classification -- 7.