Chapter 1 Introduction to Digital Image Processing of Remotely Sensed Data
     Major Characteristics of Remote Sensing systems, Table 1-2; p. 13 Image Processing considerations, Fig. 1-3;.
 Chapter 2 Remote Sensing Data Acquisition Alternatives
     p. 26-37 Characteristics of Multispectral Remote Sensing systems, Table 2-2; 2-4; p. 40-42 Characteristics of
     Thematic        Mapper Spectral Bands, Table 2-6; Fig. 2-24, 2-25
     p. 60-61 Digital Image Data Formats
 Chapter 3 Image Processing System Considerations
 Chapter 4 Initial Statistics Extraction
 Chapter 5 Initial Display alternative and Scientific Visualization
     Fig 5-8, 5-9; 5-11;
     p. 101 RGB to IHS Transformation and Back Again, Fig 5-14,15;
 Chapter 6 Image Preprocessing: radiometric and geometric correction
     Fig. 6-1, 6-3; 6-6,7;
 Chapter 7 Image Enhancement
     p. 152 Band Ratioing
 Chapter 8 Thematic Information Extraction: Image Classification
 Chapter 9 Digital Change Detection
 Chapter 10 Geographic Information Systems
     p. 282 Data Structure; Vector Data Model; Raster Data Model; Quadtree Raster Data Model; DEM; TIN; (TOSCA).

                             Remote Sensing Applications in the Geosciences - Course Notes

 The electromagnetic spectrum, 7,17
 Photographic; Thermal IR; Rador K, X, and L bands

 Elements of photo interpretation 2, 5, 9
 Tone; colour or relative brightness
  texture; frequency of tonal change produced by the imaging of aggregations of unit forms which are too small to be detected individually but give rise to repetitive structure in the image. Pattern and texture differ in terms of scale of observation.
  pattern; spatial arrangement of objects
  shape;
  size and association
  shadow
 Data processing - enhancement; decorrelation; multivariate analysis 10

Sensor Specifications
Visible spectrum 400-700 nonometers (.5 micrometers) 8, 17
Atmospheric Windows
 Spot Green 10x10 metres: .5-.59 Red .61-.68 Near-IR .79-.89 24, 25
 Landsat 60x80 metres: MSS 1 .5-.6 green; 2 .6-.7 red; 3 .7-.8 Near IR; 4 .8-1.1 Near IR26, 27
 TM 30x30 metres: .45-.53 blue; .52-.57 green; .63-.69 red; .76-.9; Near IR; 1.55-1.75 SWIR; 2.08-2.35 SWIR; 10.8-12.9 TIR (Thermal Infrared, 120x120 metres)

DATA OR IMAGE ANALYSIS

 Data Enhancement, facilitates visual interpretation 58
 Data Classification, data reduction placing features with similar spectral properties into one of a set of defined categories. 58
 Data Integration
 DEMS (Digital Elevation Model)

Visual representations of the digital image are known as domains. 59
Image domain, visual image
Frequency domain, representation of the data in the form of histograms
Feature Space domain, the coordinate system is determined by the number of bands projected and their radiometric resolution; used to develope interpreation keys.

Image Correction 60
 - Resampling techniques 64
 Nearest neighbour, transfer of gray level of nearest pixel, simple and gray levels unaltered; image blocky, pixels offset by up to half a pixel.
 Bi-linear Interpolation, transfer proximity weighted average of 4 nearest pixels, smoother and more accurate image, gray values are altered; some blurring.
 Cubic convolution, transfer evaluated weight of 16 nearest pixels, very smooth image and most accurate; gray values are altered, slow.

ENHANCEMENT 66
 Contrast stretch using LUT 68
 Linear; histogram or frequency stretch (display range assigned is proportional to the frequency[no of pixels] in the histogram segment; infrequency stretch (inverse of frequency stretch); logarithmic stretch, mid-range brightness is enhanced and histogram skew corrected; custom breakpoint stretch, deductive approach. 70, 71
 
 Gray Level Thresholding, to distinguish land and water. 72
 In Photo-Paint, pixels lighter than a specified threshold value become white, darker pixels become solid.
 Edge enhancements, enhance the visibility of existing edge features; uses a boxcar filter of 5x5 pixels; pixel brighness values are selectively altered to mark location of significant changes in brightness slope. Produces some degradation in general image contrast and quality; most valuable where information on structures are of critical importance (Adaptive Unsharp; unsharp - edge and a certain amoutn of smoothness).

Ratio Images use brightness ratios of different wavelength bands to enhance target/background contrast. Ratio values must be scaled to produce an acceptable qulity image for visual interpreation. 74

SUPERVISED CLASSIFICATION 75
Uses signals from specified training sites.
UNSUPERVISED CLASSIFICATION 76
Assigns cover types to classes based on spectral values alone; done on the basis of tone and other visual elements such as pattern and texture are not considered.
PRINCIPLE COMPONENT ENHANCEMENT 78
 Individual bands are often highly correlated. Correlation can be checked by looking at scatter plots. Principal component enhancment can be used to identify new axes which maximize variance

DATA INTEGRATION 80

 Integration of Remotely sensed data with geologic, geophysical and geochemical data 82
 Step 1 enter the digitized data into an Image Analysis System (e.g. Image Works)
 Step 2 Registration and Geometric correction (Geocoding); data pre-processing
 Step 3 Enhancements and integration to ANALYSIS, then EXTRACTION, then addition to a GIS obtained by digitizing maps; spatial analysis and modelling of geologic data.

SPATIAL ANALYSIS of combined data images.
 The ultimate goal is to produce geocoded colour images in which the colours can be meaningfully interpreted and related to terrain/geologic features ... this implies that the reflection or emission characteristics of the input data are known and that the particular integration technique selected preserves the relationship between the input data characteristics and the resulting image colours - the colours are meaningful!! 84
 SAR (Synthetic Aperture Sensor). 86
Raster integration techniques 89
 Band combinations - one band per red green and blue guns of the CRT.
 Arimetic combinations.
 Statistical combinations
 Colour Space Transforms - transformation of data into a differetn display space-colours can be quantifiably described by orthogonal axes, (intensity, saturation, hue) in display space (RGB triangle has red, green, and blue at the apices). 89

COLOUR COMPOSITE 94
RGB and IHS Colour spaces 100
IHS Black to white on vertical axis = Intensity; black to colour on horizontal axes = saturation; points of a horizontal polygon are  red, yellow green, cyan, blue, magenta hues.
The intensity vector can represent a gray value for the radar channel
Magnetic data can be represented by the hue, and radar by the intensity.
Saturation value can be set to an arbitrary value to ephasize the feature represented by the hue.
It can be set to ensure a balance between hue and intensity; geology as hue and radar image as intensity.

EQUIPMENT 143
SPECTRAL GEOLOGY 144
SPECTRAL REFLECTANCE 146
TM PROCESSING TECHNIQUES FOR DISCRIMINATING LITHOLOGY AND ALTERATION 158
COLOUR COMPOSITES
 IHS
 Decorrelation stretch see page 10
RATIO
 Single band
 Colour composites
PRINCIPAL COMPONENT ANALYSIS
CLASSIFICATION
ALTERED ROCK SPECTRA, GOLDFIELD, NEVADA 160
SULPHURETS 162
STRUCTURAL GEOLOGY 168

                                         Idrisi Tutorial Exercises

 1. The IDRISI for Windows environment
 2. The display system
 3. Map composition
 4. Palettes, symbols and scaling
 5. Database query
 6. Map algebra
 7. Database workshop
 8. Distance and context operators
 9. Automating analyses with macros
 10. Cost distances and least cost pathways
 11. Image exploration
 12. Supervised classification
 13. Principal components analysis
 14. Unsupervised classification
 15. Image georegistration using RESAMPLE
 16. Digital cartographic databases
 17. Changing reference systems with PROJECT.

                                         PCI - Using PCI Software, V. 1, 2

 1. User Interfaces
 2. Video Display: concepts and use
 3. Database Management
 4. Projections
 5. Importing data
 6. Preprocessing and Geometric correction
 7. Enhancements: contrast manipulation
 8. Enhancements: spatial filtering
 9. Enhancements: multi-image manipulations
 10. Image Classification
 11. Working with vectors
 12. Working with Attribute data
 13. Generating and Working with DEMs
 14. Orthorectification and DEM extraction
 15. Hyperspectral Analysis
 16. SAR (Synthetic Aperture) analysis
 17. Spatial Modelling: Raster GIS
 18. Data Presentation
 19. Exporting and Archiving Data
 App. A. DCP: Display Control Program
 App. B Glossary of Terms

                             ER Mapper 5.0 Tutorial

 Table of contents
1. Introduction ot ERMAPPER -1
2. User interface basics - 11
3. Creating an algorithm -27
4. Working with data layers - 43
5. Viewing image data values - 59
6. Enhancing image contrast - 67
7. Using spacial filters - 89
8. Using formulas - 101
9. Geolinking images - 119
10. Writing images to disk - 137
11. Colourdraping images (Translucent layers) - 145
12. Mosaocking images - 159
13. Virtual datasets - 175
14. 3-D perspective viewing - 191
15. Thematic raster overlays - 201
16. Composing maps - 213
17. Unsupervised classification - 229
18. Supervised classificiation - 241
19. Raster to vector conversion - 259
A System setup - 269
B Reference texts - 271
Index - 273

     Procedures
        Data import
 Raster data includes satellite and aerial images, DTM’s and geophysical data
 An ERM data set has two files: a binary BIL file and a header .ers file.
 Vector data is stored as lines, points and polygons; as an ASCII data file and an .erv ASCII header file.

        Image display
 Display format is called colour mode, RGB or HSI
 Also involves the display of statistical information about the image, e.g. histograms.
 
        Image registration/rectification
 Removal of geometric errors, alignment with real world projections, and the geometric alignment of two or more images.
 
        Image mosaicking
 assembly of several adjacent images into a single image.

        Image enhancement (processing)
 Image merging, e.g. TM and SPOT; colour draping, e.g. vegetation over gravity as z; contrast enhancement; filtering; formula processing (algebraic manipulation); classification.

        Dynamic Links overlays
 Links to other file formats without need to convert files.

        Annotation and map composition
 Add vector data by drawing directly on screen.

        Data export and hardcopy printing
 
        Image Processing Tasks - p. 5

     Each Image file(s) is contained in its own directory, and the files referred to in the directories are algorithms used to manipulate the image.
     Mouse manipulation e.g. double-click to select, drag the title bar, resize window;    GUI tools, e.g. zoom, select active window; and    commands, e.g. FILE menu Open, are mostly standard windows-like operations. However note:
 zoom uses a marquee
     click the image with the left mouse button to pan such that the clicked point becomes the centre of the image;
     shift-click to zoom out;
     use View and Quick zoom to zoom to extents, and View Geoposition to use a set of zoom buttons;

                                 Geographic Information Systems for Geoscientists - Bonham-Carter

                    Chapter 1
     p. 1-3. GIS is simply a computer system for managing spatial data. GIS have capabilities for data capture, input, manipulation, transformation, visualization, combination, query, analysis, modelling and output. The ultimate purpose is to provide support for making decisions based on spatial data.
         Use a GUI or a command language.
         Data is geocoded, which means it is geographically located.
         Different geocoded data sets are spatially registered, that is they overlap correctly.
 There may be a straight line constant ratio relationship between two elements, but the spatial distribution of the primary values may be nodal or random or linear contoured or patterned, etc. Or the ratio may be bimodal both graphically and spatially.
     p. 3 Visualization reveals spatial patterns.
     p. 4 Spatial query allows the asking of the questions :
         What are characteristics of this location?
         Whereabouts do these characteristics occur? (Where do gold and sulphur occur together?)
     p. 5 Combination merges spatial datasets, e.g. a geological map and a satellite image.
     p. 5 Analysis e.g. trend surface analysis.
     p. 6 Prediction e.g. what are the parameters defining sites of gold mineralization.
     p. 14-22 A Model GIS Study for Mineral Potential Mapping.
     p. 32-39 Raster and Vector Spatial Data Models
     p. 39-43 Attribute Data.
     p. 43-49 The Relational model.
     p. 52-68 Raster Structures.
     p. 68-81 Vector Data Structures (TOSCA)
     p. 87-90 Map Projections.
     p. 95-101 Digitizing.
     p. 103-108 Coordinate Conversion.
     p. 120-126 Colour, Colour lookup tables.
     p. 186-202 Map Reclassification.
     p. 204-210 Operations on Spatial Neighbourhoods.
     p. 267 271 Map Analysis, Types of Models.
     p. 272 Boolean Logic Models, Landfill Site Selection, Mineral Potential Evaluation.