Keep your course current and relevant Content updates Chapter 1: Introduction Greater emphasis is now placed on the importance of ground reference information that can be used to calibrate remote sensor data and assess the accuracy of remote sensing-derived products such as thematic maps. The "Remote Sensing Process" has been updated to reflect recent innovations in digital image processing. Greater emphasis is now placed on the use of remote sensing to solve local, high-spatial resolution problems as well as for use in global climate change research. This chapter now includes detailed information about the increasing demand for people trained in remote sensing digital image processing. Information is provided from a) the NRC (2013) Future U.S. Workforce for Geospatial Intelligence study, and b) U.S.
Department of Labor Employment and Training Administration (USDOLETA, 2014) data about the 39,900 "Remote Sensing Scientists and Technologists" and "Remote Sensing Technicians" job openings projected from 2012-2022. Most of these occupations require training in remote sensing digital image processing. Chapter 2: Remote Sensing Data Collection This chapter provides information about historical, current, and projected sources of remotely sensed data. Detailed information about new and proposed satellite remote sensing systems (e.g., Astrium''s Pleiades and SPOT 6; DigitalGlobe''s GeoEye-1, GeoEye-2, World-View-1, WorldView-2, WorldView-3; India''s CartoSat and ResourceSat; Israel''s EROS A2; Korea''s KOMPSAT; NASA''s Landsat 8; NOAA''s NPOESS; RapidEye, etc.) and airborne remote sensing systems (e.g.
, PICTOMETRY, Microsoft''s UltraCAM, Leica''s Airborne Digital System 80) are included in the fourth edition. Technical details about decommissioned (e.g., SPOT 1, 2; Landsat 5), degraded (e.g., Landsat 7 ETM+) or failed (e.g., European Space Agency Envisat) sensor systems are provided.
Chapter 3: Digital Image Processing Hardware and Software As expected, the computer hardware (e.g., CPUs, RAM, mass storage, digitization technology, displays, transfer/storage technology) and software [e.g., multispectral, hyperspectral, per-pixel, object-based image analysis (OBIA)] necessary to perform digital image processing have progressed significantly since the last edition. Improvements in computer hardware often used to perform digital image processing are discussed. The most important functions, characteristics and sources of the major digital image processing software are provided. Chapter 4: Image Quality Basic digital image processing mathematical notation is reviewed along with the significance of the histogram.
The importance of metadata is introduced. Visual methods of assessing image quality are presented including three-dimensional representation. Univariate and multivariate methods of assessing the initial quality of digital remote sensor data are refreshed. A new section on geostatistical analysis, autocorrelation and kriging interpolation is provided. Chapter 5: Display Alternatives and Scientific Visualization New information is provided on: liquid crystal displays (LCD), image compression alternatives, color coordinate systems (RGB, Intensity-Hue-Saturation, and Chromaticity), the use of 8- and 24-bit color look-up tables, and new methods of merging (fusing) different types of imagery (e.g., Gram-Schmidt, regression Kriging). Additional information is provided about measuring distance, perimeter, shape and polygon area using digital imagery.
Chapter 6: Radiometric Correction Additional information is provided about electromagnetic radiation principles (e.g., Fraunhofer absorption features) and the spectral reflectance characteristics of selected natural and human-made materials. Updated information about the most important radiometric correction algorithms is provided, including: a) those that perform absolute radiometric correction (e.g., MODTRAN 4, ACORN, FLAASH, QUAC, ATCOR, empirical line calibration) and, b) those that perform relative radiometric correction (e.g., single and multiple- date image normalization).
Chapter 7: Geometric Correction Traditional as well as improved methods of image-to map rectification and image-to-image registration are provided. In addition, this edition contains an expanded discussion on developable surfaces and the properties and advantages/disadvantages of several of the most heavily used cylindrical, azimuthal, and conical map projections. MODIS satellite imagery is projected using selected map projections (e.g., Mercator, Lambert Azimuthal Equal-area). The image mosaicking section contains new examples and demonstrates the characteristics of the USGS annual mosaic of Landsat ETM+ data (i.e., the WELD: Web-enabled Landsat Data project).
Chapter 8: Image Enhancement The image magnification and reduction sections are revised. In addition, the following image enhancement techniques are updated: band ratioing, neighborhood raster operations, spatial convolution filtering and edge enhancement, frequency filtering, texture extraction, and Principal Components Analysis (PCA). The vegetation indices (VI) section has been significantly revised to include new information on the dominant factors controlling leaf reflectance and the introduction of numerous new indices with graphic examples. Several new texture transforms are introduced (e.g., Moran''s I Spatial Autocorrelation) and new information is provided on the extraction of texture from images using Grey-level Co-occurrence Matrices (GLCM). The chapter concludes with a new discussion on landscape ecology metrics that can be extracted from remotely sensed data. Chapter 9: Information Extraction Using Pattern Recognition Updated information on the American Planning Association Land-Based Classification Standard (NLCS), the U.
S. National Land Cover Database (NLCD) Classification System, NOAA''s Coastal Change Analysis Program (C-CAP) Classification Scheme, and the IGBP Land-Cover Classification System is included. New methods of feature (band) selection are introduced (e.g., Correlation Matrix Feature Selection). Additional information is provided on Object-based Image Analysis (OBIA) classification methods, including new OBIA application examples. Chapter 10: Information Extraction Using Artificial Intelligence New information is provided on image classification using machine-learning decision trees, regression trees, Random Forest (trees), and Support Vector Machines (SVM). Detailed information is now provided on a number of machine-learning, data-mining decision tree/regression tree programs that can be used to develop production rules (e.
g., CART, S-Plus, R Development Core Team, C4.5, C5.0, Cubist). New information about advances in neural network analysis of remote sensor data is included for Multi-layer Perceptrons, Kohonen''s Self-Organizing Map, and fuzzy ARTMAP neural networks. A new discussion about the advantages and disadvantages of artificial neural networks is provided. Chapter 11: Information Extraction Using Imaging Spectroscopy Advances in airborne and satellite hyperspectral data collection are discussed. Advances in the methods used to process and analyze hyperspectral imagery are provided, including: end-member selection and analysis, mapping algorithms, Spectral Mixture Analysis (SMA), continuum removal, spectroscopic library matching techniques, machine-learning hyperspectral analysis techniques, new hyperspectral indices, and derivative spectroscopy.
Chapter 12: Change Detection This book has always contained detailed digital change detection information. New information is provided on the impact of sensor system look angle and amount of tree or building obscuration. Advances in binary "change/no-change" algorithms are provided including new analytical methods used to identify the change thresholds and new commercial change detection products such as ESRI''s Change Matters and MDA''s National Urban Change Indicator. Significant advances in thematic "from-to" change detection algorithms are discussed including photogrammetric and LiDARgrammetric change detection, OBIA post-classification comparison change detection, and Neighborhood Correlation Image (NCI) change detection. Chapter 13: Remote Sensing derived Thematic Map Accuracy Assessment There is a significant amount of literature and debate about the best method(s) to use to determine the accuracy of remote sensing-derived thematic map produced from a single date of imagery or a thematic map derived from multiple dates of imagery (i.e., change detection). The accuracy assessment alternatives and characteristics of the debate are discussed more thoroughly.
Appendix: Sources of Imagery and other Geospatial Information A new appendix is provided that contains a list of selected geospatial datasets that can be evaluated and/ or downloaded via the Internet, including: digital elevation information, hydrology, land use/land cover and biodiversity/habitat, road network and population demographic data, and several types of publicly- and commercially-available remote sensor-data. Map or image examples of the datasets are presented where appropriate.