The Nature of Remote Sensing: Introduction. Remote Sensing. Information Extraction from Remote-Sensing Images. Spectral Factors in Remote Sensing. Spectral Signatures. Remote-Sensing Systems. Optical Sensors. Temporal Characteristics. Image Display Systems. Data Systems. Summary. Exercises. References. Optical Radiation Models: Introduction. Visible to Short Wave Infrared Region. Solar Radiation. Radiation Components. Surface-Reflected. Unscattered Component. Surface-Reflected. Atmosphere-Scattered Component. Path-Scattered Component. Total At-Sensor. Solar Radiance. Image Examples in the Solar Region. Terrain Shading. Shadowing. Atmospheric Correction. Midwave to Thermal Infrared Region. Thermal Radiation. Radiation Components. Surface-Emitted Component. Surface-Reflected. Atmosphere-Emitted Component. Path-Emitted Component. Total At-Sensor. Emitted Radiance. Total Solar and Thermal Upwelling Radiance. Image Examples in the Thermal Region. Summary. Exercises. References. Sensor Models: Introduction. Overall Sensor Model. Resolution. The Instrument Response. Spatial Resolution. Spectral Resolution. Spectral Response. Spatial Response. Optical PSFopt. Image Motion PSFIM. Detector PSFdet. Electronics PSFel. Net PSFnet. Comparison of Sensor PSFs. PSF Summary for TM. Imaging System Simulation. Amplification. Sampling and Quantization. Simplified Sensor Model. Geometric Distortion. Orbit Models. Platform Attitude Models. Scanner Models. Earth Model. Line and Whiskbroom ScanGeometry. Pushbroom Scan Geometry. Topographic Distortion. Summary. Exercises. References. Data Models: Introduction. A Word on Notation. Univariate Image Statistics. Histogram. Normal Distribution. Cumulative Histogram. Statistical Parameters. Multivariate Image Statistics. Reduction to Univariate Statistics. Noise Models. Statistical Measures of Image Quality. Contrast. Modulation. Signal-to-Noise Ratio (SNR). Noise Equivalent Signal. Spatial Statistics. Visualization of Spatial Covariance. Covariance with Semivariogram. Separability and Anisotropy. Power Spectral Density. Co-occurrence Matrix. Fractal Geometry. Topographic and Sensor Effects. Topography and Spectral Statistics. Sensor Characteristics and Spectral Stastistics. Sensor Characteristics and Spectral Scattergrams. Summary. Exercises. References. Spectral Transforms: Introduction. Feature Space. Multispectral Ratios. Vegetation Indexes. Image Examples. Principal Components. Standardized Principal Components (SPC) Transform. Maximum Noise Fraction (MNF) Transform. Tasseled Cap Tranformation. Contrast Enhancement. Transformations Based on Global Statistics. Linear Transformations. Nonlinear Transformations. Normalization Stretch. Reference Stretch. Thresholding. Adaptive Transformation. Color Image Contrast Enhancement. Min-max Stretch. Normalization Stretch. Decorrelation Stretch. Color Spacer Transformations. Summary. Exercises. References. Spatial Transforms: Introduction. An Image Model for Spatial Filtering. Convolution Filters. Low Pass and High Pass Filters. High Boost Filters. Directional Filters. The Border Region. Characterization of Filtered Images. The Box Filter Algorithm. Cascaded Linear Filters. Statistical Filters. Gradient Filters. Fourier Synthesis. Discrete Fourier Transforms in 2-D. The Fourier Components. Filtering with the Fourier Transform. Transfer Functions. The Power Spectrum. Scale Space Transforms. Image Resolution Pyramids. Zero-Crossing Filters. Laplacian-of-Gaussian (LoG) Filters. Difference-of-Gaussians (DoG) Filters.Wavelet Transforms. Summary. Exercises. References. Correction and Calibration: Introduction. Noise Correction. Global Noise. Sigma Filter. Nagao-Matsuyama Filter. Local Noise. Periodic Noise. Distriping 359. Global,Linear Detector Matching. Nonlinear Detector Matching. Statistical Modification to Linear and Nonlinear Detector. Matching. Spatial Filtering Approaches. Radiometric Calibration. Sensor Calibration. Atmospheric Correction. Solar and Topographic Correction. Image Examples. Calibration and Normalization of Hyperspectral Imagery. AVIRIS Examples. Distortion Correction. Polynomial Distortion Models. Ground Control Points (GCPs). Coordinate Transformation. Map Projections. Resampling. Summary. Exercises References. Registration and Image Fusion: Introduction. What is Registration? Automated GCP Location. Area Correlation. Other Spatial Features. Orthrectification. Low-Resolution DEM. High-Resolution DEM. Hierarchical Warp Stereo. Multi-Image Fusion. Spatial Domain Fusion. High Frequency Modulation. Spectral Domain Fusion. Fusion Image Examples. Summary. Exercises. References. Thematic Classification: Introduction. The Importance of Image Scale. The Notion of Similarity. Hard Versus Soft Classification. Training the Classifier. Supervised Training. Unsupervised Training. K-Means Clustering Algorithm. Clustering Examples. Hybrid Supervised/Unsupervised Training. Non-Parametric Classification Algorithms. Level-Slice. Nearest-Mean. Artificial Neural Networks (ANNs). Back-Propagation Algorithm. Nonparametric Classification Examples. Parametric Classification Algorithms. Estimation of Model-Parameters. Discriminant Functions. The Normal Distribution Model. Relation to the Nearest-Mean Classifier. Supervised Classification Examples and Comparison to Nonparametric Classifiers. Segmentation. Region Growing. Region Labeling. Sub-Pixel Classification. The Linear Mixing Model. Unmixing Model. Hyperspectral Image Analysis. Visualization of the Image Cube. Feature Extraction. Image Residuals. Pre-Classification Processing and Feature Extraction. Classification Algorithms. Exercises. Error Analysis. Multitemporal Images. Summary. References. Index.

Remote sensing, models, and methods for image processing

This book is an integrated sequence of papers Professor Michie has published, for a general audience, over the last decade or so. It contains the following: Introduction Trial and Error (Science Survey, 1961) Puzzle-learning versus Game-learning (The Scientist Speculates, 1962) Game-playing Automata (Advances in Programming and Non-Numerical Computation, 1966) Machines that Play and Plan (Science Journal, 1968) Computer servant or master (Theoria to Theory, 1968) Integrated Cognitive Systems (Nature, 1970) Tokyo-Edinburgh Dialogue (The Computer Journal, 1971) Artificial Intelligence (New Society, 1971) On not seeing things (Experimental Programming Report, 1971) Programmer's Gambit (New Scientist, 1972) Machine Intelligence at Edinburgh (Management Informatics, 1973) Theory of Intelligence (Nature, 1973) Memory Mechanisms and Learning (Simple Nervous Systems, 1974) Knowledge Engineering (Kybernetics, 1973) Maching Intelligence as Technology (Proceedings of the Conference on Shop Floor Automation, 1973) 7. The Structure of Belief Systems -Robert P. Abelson

Syntactic Methods in Pattern Recognition

In the past, picture segmentation has been performed by merging small primitive regions or by recursively splitting the whole picture. This paper combines the two approaches with significant increase in processing speed while maintaining small memory requirements. The data structure is described in detail and examples of implementations are given.

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Picture Segmentation by a Tree Traversal Algorithm

Relative or absolute elevation extraction from satellite radar data has been an active research topic for more than 20 years. Various investigations have been made on different methods depending on the predominant “fashion” and data availability, leading each time to new developments to improve the capability and the applicability of each method. The paper presents an update of the state-of-the-art of elevation extraction from satellite SAR data. The performance and limitations of four different methods (clinometry, stereoscopy, interferometry and polarimetry) are reviewed, as well as their applicability to different satellite SAR sensors. Their advantages and disadvantages and how they are addressed during the data processing are also analysed. Finally, concluding remarks look at the complementarity aspects of each method to make the best use of the existing and future radar data for elevation extraction.

State-of-the-art of elevation extraction from satellite SAR data

Topological characterizations of sets of graphs which can be generated by contextfree or linear grammars are given. It is shown, for example, that the set of all planar graphs cannot be generated by a context-free grammar while the set of all outerplanar graphs can

Linear and Context-Free Graph Grammars

During the SIR-B mission in October 1984, a significant number of overlapping synthetic aperture radar (SAR) images of various ground areas was collected. This has offered the first opportunity to perform stereo analyses on images from space that cover large ground areas to determine elevation information. This paper presents the preliminary results of an investigation to obtain elevation data from stereo pairs of SIR-B images. First, the accuracy with which elevation information can be derived from SIR-B image pairs is evaluated theoretically. It is shown that elevation accuracy is a function of the slant range resolution, the incidence angles with which the stereo pair is obtained, the accuracies in spacecraft state estimation, and determination of corresponding pixels in the stereo pair. Next, a hierarchical method is developed to match the corresponding pixels. This method involves iterative removal of local distortions and correlations of pairs of local neighborhoods in the two images. Since it is necessary to perform the matching at every pixel in the image, it is very computationally intensive. Therefore, it has been implemented on the Massively Parallel Processor (MPP) at the Goddard Space Flight Center (GSFC). The MPP's speed permits two iterations of this technique to operate on a pair of 512 × 512 images within 7 s. Results of applying this algorithm to SIR-B images of Mount Shasta, CA, are shown. The matching algorithm performs well in regions of the image with significant features.

Automated Matching of Pairs of SIR-B Images for Elevation Mapping

Publisher Summary This chapter describes a grammar for maps. Grammars whose sentences are not strings of symbols, but rather sets of symbols that can be interconnected in more general ways, are of importance in connection with the formal theory of picture processing and description. One of the most general formalisms of this kind deals with sentences, which are labeled directed graphs, that is, webs. Webs arise naturally in connection with descriptions of pictures; a description can present the relations among objects or regions in the given picture, so that it can be represented by a web whose vertices represent regions and whose edges indicate related pairs of regions. As pictures are planar, the webs that arise in this way can mostly be planar. When a graph is used to represent the adjacency relation between regions in the plane, not all of the topological information about the regions is preserved.

A Grammar for Maps

A method of converting a picture into a “cartoon” or “map” whose regions correspond to differently textured regions is described. Texture edges in the picture are detected, and solid regions surrounded by these (usually broken) edges are “colored in” using a propagation process. The resulting map is cleaned by comparing the region colors with the textures of the corresponding regions in the picture, and also by merging some regions with others according to criteria based on topology and size. The method has been applied to the construction of cloud cover maps from cloud cover pictures obtained by satellites.

A region coloring technique for scene analysis

Binary image operations using non-coherent optical techniques

A method of converting a picture into a "cartoon" or "map" whose regions correspond to differently textured regions is described. Texture edges in the picture are detected, and solid regions surrounded by these (usually broken) edges are "colored in" using a propagation process. The resulting map is cleaned by comparing the region colors with the textures of the corresponding regions in the picture, and also by merging some regions with others according to criteria based on topology and size. The method has been applied to the construction of cloud cover maps from cloud cover pictures obtained by satellites.

James P. Strong

Papers

Automated Matching of Pairs of SIR-B Images for Elevation Mapping

A Grammar for Maps

A region coloring technique for scene analysis

Binary image operations using non-coherent optical techniques

Automatic cloud cover mapping