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

In the field of transportation, data analysis is probably the most important and widely used research tool available. In the data analysis universe, there are two ‘schools of thought’; the first uses statistics as the tool of choice, while the second – one of the many methods from – Computational Intelligence. Although the goal of both approaches is the same, the two have kept each other at arm’s length. Researchers frequently fail to communicate and even understand each other’s work. In this paper, we discuss differences and similarities between these two approaches, we review relevant literature and attempt to provide a set of insights for selecting the appropriate approach.

Statistical methods versus neural networks in transportation research: Differences, similarities and some insights

The past decade or so has seen a dramatic change in the way that economists can learn by watching our planet from above A revolution has taken place in remote sensing and allied fields such as computer science, engineering, and geography Petabytes of satellite imagery have become publicly accessible at increasing resolution, many algorithms for extracting meaningful social science information from these images are now routine, and modern cloud-based processing power allows these algorithms to be run at global scale This paper seeks to introduce economists to the science of remotely sensed data, and to give a flavor of how this new source of data has been used by economists so far and what might be done in the future

https://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.30.4.171

The View from Above: Applications of Satellite Data in Economics

This research seeks to contribute to advancing qualitative methodologies at the intersection of qualitative geographic information systems (GIS), narrative analysis, 3D GIS-based time-geographic methods, and computer-aided qualitative data analysis. The approach to GIS-based narrative analysis developed in the study, called “geo-narrative,” is based on extending current GIS capabilities for the analysis and interpretation of narrative materials such as oral histories, life histories, and biographies. The three central elements in this approach are (1) narrative analysis as the qualitative approach; (2) 3D GIS-based time-geographic methods as the representational framework; and (3) 3D-VQGIS as the GIS-based computer-aided qualitative data analysis component. A case example based on a study of the lives of the Muslim women in Columbus, Ohio, after 11 September 2001 is used to illustrate the approach.

Geo-Narrative: Extending Geographic Information Systems for Narrative Analysis in Qualitative and Mixed-Method Research∗

Flattening the Earth, Two Thousand Years of Map Projections

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO-GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi-automatically tract metadata from syntactically (including unstructured, semi-structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.

/pdf/geospatial-ontology-development-and-semantic-analytics-20ni7yug8h.pdf

Geospatial Ontology Development and Semantic Analytics

This study compares the performance of multilayer perceptron neural networks and maximum-likelihood doubly-constrained models for commuter trip distribution. Our experiments produce overwhelming evidence at variance with the existing literature that the predictive accuracy of neural network spatial interaction models is inferior to that of maximum-likelihood doubly-constrained models with an exponential function of distance decay. The study points to several likely causes of neural network underperformance, including model non-transferability, insufficient ability to generalize, and reliance on sigmoid activation functions, and their inductive nature. It is concluded that current perceptron neural networks do not provide an appropriate modeling approach to forecasting trip distribution over a planning horizon for which distribution predictors (number of workers, number of residents, commuting distance) are beyond their base-year domain of definition.

Trip distribution forecasting with multilayer perceptron neural networks: a critical evaluation

The reprojection of image data causes the loss or duplication of original pixel values. This research investigated the feasibility of using the sinusoidal projection for global image database construction. Specifically, reprojection accuracies were tested with geographic latitude and longitude coordinates, and the Hammer-Aitoff, Eckert IV, Mollweide, and sinusoidal projections. Reprojections between these five global projections and the Universal Transverse Mercator (UTM) projection and referencing system were performed using fifty-four sample datasets. A statistical analysis of categorical accuracy, a measure describing the omission of pixel values during reprojection, was conducted. Geographic coordinates and the sinusoidal projection both showed very high accuracy rates (100.0 percent and 99.5 percent respectively) when sample datasets were reprojected from UTM. The geographic coordinates, however, showed very low accuracy (65.3 percent) when sample datasets were reprojected to the UTM projection, whil...

The Sinusoidal Projection: A New Importance in Relation to Global Image Data

Investigations of the cartographic quality of Landsat-4 MSS and TM image data in CCT-p formats have produced rectification accuracies (rmsexy values) of ± 2/3 to ± 1 data pixel for both whole and subscene areas using polynomials of the first through third degree. In order to achieve these accuracies with MSS data, 15 or more Ground Control Points (GCP's) are required, whereas with the TM data sets as few as 5-10 GCPs will suffice. Factors which limit the cartographic rectification accuracies of the Landsat-4 data include: 1) spatial resolution of the data; 2) map and digitizing errors; and 3) terrain relief. Of these factors, data resolution is the most significant, limiting the location of GCP's to about ± 0.5 pixel. Horizontal displacements due to terrain relief can be minimized by selecting GCP's at or near midrange elevations. Overall, the representative rmsexy values of ±25 and ±55 m for TM and MSS data sets are within U.S. National Map Accuracy Standards for cartographic products of 1:100 000 and 1:200 000 scale, respectively.

E. Lynn Usery

Papers

Geospatial Ontology Development and Semantic Analytics

Trip distribution forecasting with multilayer perceptron neural networks: a critical evaluation

The Sinusoidal Projection: A New Importance in Relation to Global Image Data

Cartographic Accuracy of LANDSAT-4 MSS And TM Image Data

Knowledge-based GIS techniques applied to geological engineering