Computer Processing of Remotely-sensed Images

Paul Mather has been Professor of Geographical Information Science at The University of Nottingham, UK since 1988. BA (Cambridge), PhD (Nottingham). Main research interests: multivariate analysis of spatial data (Computational Methods of Multivariate Analysis in Physical Geography, Chichester, John Wiley, 1976) and digital image processing applied to remotely sensed data, particularly algorithms for classification and pattern recognition (Classification Methods for Remotely Sensed Data, with B. Tso, London, Taylor and Francis, 2001). * Awarded OBE in the Queen's Jubilee Honours in 2002 for 'services to remote sensing and photogrammetry'. * Recipient of The Remote Sensing Society's Gold Medal in 1996. * Presented with the Back Award of the Royal Geographical Society for 'contributions to remote sensing' in 1992. * Winner of the Dearing Award for "Outstanding contribution to the development of teaching and learning", The University of Nottingham, 2003. * Invited speaker/lecturer at conferences/courses all over the world (e.g., Japan, Indonesia, India, South Africa, Poland, Hungary, Bulgaria, Spain).

Preface to the First Edition. Preface to the Second Edition. Preface to the Third Edition. List of Examples. 1. Remote Sensing: Basic Principles. 1.1 Introduction. 1.2 Electromagnetic radiation and its properties. 1.2.1 Terminology. 1.2.2 Nature of electromagnetic radiation. 1.2.3 The electromagnetic spectrum. 1.2.4 Sources of electromagnetic radiation. 1.2.5 Interactions with the Earth's atmosphere. 1.3 Interaction with Earth-surface materials. 1.3.1 Introduction. 1.3.2 Spectral reflectance of Earth surface materials. 1.4 Summary. 2. Remote Sensing Platforms and Sensors. 2.1 Introduction. 2.2 Characteristics of imaging remote sensing instruments. 2.2.1 Spatial resolution. 2.2.2 Spectral resolution. 2.2.3 Radiometric resolution. 2.3 Optical, near-infrared and thermal imaging sensors. 2.3.1 Along-Track Scanning Radiometer (ATSR). 2.3.2 Advanced Very High Resolution Radiometer (AVHRR). 2.3.3 MODIS (MODerate Resolution Imaging Spectrometer). 2.3.4 Ocean observing instruments. 2.3.5 IRS-1 LISS. 2.3.6 Landsat Instruments. 2.3.7 SPOT sensors. 2.3.8 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). 2.3.9 High-resolution commercial and micro-satellite systems. 2.4 Microwave imaging sensors. 2.4.1 ERS SAR. 2.4.2 RADARSAT. 2.5 Summary. 3. Hardware and Software Aspects of Digital Image Processing. 3.1 Introduction. 3.2 Properties of digital remote sensing data. 3.2.1 Digital data. 3.2.2 Data formats. 3.2.3 System processing. 3.3 MIPS software. 3.3.1 Installing MIPS. 3.3.2 Using MIPS. 3.3.3 Summary of MIPS functions. 3.4 Summary. 4. Pre-processing of Remotely Sensed Data. 4.1 Introduction. 4.2 Cosmetic operations. 4.2.1 Missing scan lines. 4.2.2 De-striping methods. 4.3 Geometric correction and registration. 4.3.1 Orbital geometry model. 4.3.2 Transformation based on ground control points. 4.3.3 Resampling procedures. 4.3.4 Image registration. 4.3.5 Other geometric correction methods. 4.4 Atmospheric correction. 4.4.1 Background. 4.4.2 Image-based methods. 4.4.3 Radiative transfer models. 4.4.4 Empirical line method. 4.5 Illumination and view angle effects. 4.6 Sensor calibration. 4.7 Terrain effects. 4.8 Summary. 5. Image Enhancement Techniques. 5.1 Introduction. 5.2 Human visual system. 5.3 Contrast enhancement. 5.3.1 Linear contrast stretch. 5.3.2 Histogram equalisation. 5.3.3 Gaussian Stretch. 5.4 Pseudocolour enhancement. 5.4.1 Density slicing. 5.4.2 Pseudocolour transform. 5.5 Summary. 6. Image Transforms. 6.1 Introduction. 6.2 Arithmetic operations. 6.2.1 Image addition. 6.2.2 Image subtraction. 6.2.3 Image multiplication. 6.2.4 Image division and vegetation Indices. 6.3 Empirically based image transforms. 6.3.1 Perpendicular Vegetation Index. 6.3.2 Tasselled Cap (Kauth-Thomas) transformation. 6.4 Principal Components Analysis. 6.4.1 Standard Principal Components Analysis. 6.4.2 Noise-adjusted Principal Components Analysis. 6.4.3 Decorrelation stretch. 6.5 Hue, Saturation and Intensity (HIS) transform. 6.6 The Discrete Fourier Transform. 6.6.1 Introduction. 6.6.2 Two-dimensional DFT. 6.6.3 Applications of the DFT. 6.7 The Discrete Wavelet Transform. 6.7.1 Introduction. 6.7.2 The one-dimensional Discrete Wavelet Transform. 6.7.3 The two-dimensional Discrete Wavelet Transform. 6.8 Summary. 7. Filtering Techniques. 7.1 Introduction. 7.2 Spatial domain low-pass (smoothing) filters. 7.2.1 Moving average filter. 7.2.2 Median filter. 7.2.3 Adaptive filters. 7.3 Spatial domain high-pass (sharpening) filters. 7.3.1 Image subtraction method. 7.3.2 Derivative-based methods. 7.4 Spatial domain edge detectors. 7.5 Frequency domain filters. 7.6 Summary. 8. Classification. 8.1 Introduction. 8.2 Geometrical basis of classification. 8.3 Unsupervised classification. 8.3.1 The k-means algorithm. 8.3.2 ISODATA. 8.3.3 A modified k-means algorithm. 8.4 Supervised classification. 8.4.1 Training samples. 8.4.2 Statistical classifiers. 8.4.3 Neural Classifiers. 8.5 Fuzzy classification and linear spectral unmixing. 8.5.1 The linear mixture model. 8.5.2 Fuzzy classifiers. 8.6 Other approaches to image classification. 8.7 Incorporation of non-spectral features. 8.7.1 Texture. 8.7.2 Use of external data. 8.8 Contextual information. 8.9 Feature selection. 8.10 Classification accuracy. 8.11 Summary. 9. Advanced Topics. 9.1 Introduction. 9.2 SAR Interferometry. 9.2.1 Basic principles. 9.2.2 Interferometric processing. 9.2.3 Problems in SAR interferometry. 9.2.4 Applications of SAR interferometry. 9.3 Imaging spectrometry. 9.3.1 Introduction. 9.3.2 Processing imaging spectrometer data. 9.4 Lidar. 9.4.1 Introduction. 9.4.2 Lidar details. 9.4.3 Lidar applications. 9.5 Summary. Appendix A: Using the CD-ROM Image Data Sets. References. Index.