Elements of Image Interpretation
• Image interpretation requires explicit recognition of eight elements of image interpretation that form the framework and understanding of an image
Ø Shape
Ø Size
Ø Tone
Ø Texture
Ø Shadow
Ø Site
Ø Association
Ø Pattern
Shape
Shape refers to the general form, structure, or outline of individual objects.
Shape can be a very distinctive clue for interpretation
Many natural and human-made features have unique shapes.
Often used are adjectives like linear, curvilinear, circular, elliptical, radial, square, rectangular, triangular, hexagonal, star, elongated, and amorphous.
• Shadow
• They are helpful in interpretation as it may provide an idea of the profile and relative height of a target or targets
• However, shadows can also reduce or eliminate interpretation in their area of influence, since targets within shadows are much less discernible from their surroundings
• Shadow is also useful for enhancing or identifying topography and landforms.
• Size
• Size of objects in an image is a function of scale. It is important to assess the size of a target relative to other objects in a scene, as well as the absolute size, to aid in the interpretation of that target.
• For example, to distinguish zones of land use, an area with a number of buildings in it, large buildings such as factories or warehouses would suggest commercial property, whereas small buildings would indicate residential use
• Tone/ Color
• Refers to the average brightness of an area or, in the case of color imagery, to the dominant color of the region
–Depends on the nature of the surface in the ankles of observation and illumination.
–Smooth surfaces behave like specular reflectors, they tend to reflect radiation in a single direction
• These features may appear bright or dark
–Rough surfaces behave as diffuse reflectors.
• Scatter radiation in all directions.
• Texture
• Texture refers to the arrangement and frequency of tonal variation in particular areas of an image.
• Rough textures would consist of a tone where the grey levels change abruptly in a small area. Whereas smooth textures would have very little tonal variation.
• Smooth textures are such as fields, water, or grasslands. Rough surfaces are such as a forest canopy, results in a rough textured appearance
• Pattern
• Refers to the spatial arrangement of visibly discernible objects in an image.
• Typically an orderly repetition of similar tones and textures will produce a distinctive and ultimately recognizable pattern.
• Orchards with evenly spaced trees and urban streets with regularly spaced houses are good examples of the pattern.
• Association
• This takes into account the relationship between other recognizable objects or features in proximity to the target of interest.
• The identification of features that one would expect to associate with other features may provide information to facilitate identification.
• For example, commercial properties may be associated with proximity to major transportation routes, whereas residential areas would be associated with schools, playgrounds, and sports fields.
• Site
• Refers to a feature's position with respect to topography and drainage.
– Some things occupy a distinctive topographic position because of their function
• Sewage treatment facilities at the lowest feasible topographic position.
• Power plants located adjacent to water for cooling
Satellite Image Processing and Analysis
• Many image processing and analysis techniques have been developed to aid the interpretation of remote sensing images and to extract as much information as possible from the images.
• The choice of specific techniques or algorithms to use depends on the goals of each individual project
• Prior to data analysis, initial processing of the raw data is usually carried out to correct for any distortion due to the characteristics of the imaging system and imaging conditions
• These procedures include:
• Radiometric correction to correct for uneven sensor response over the whole image and
• Geometric correction to correct for geometric distortion due to Earth's rotation and other imaging conditions (such as oblique viewing)
Image processing techniques
• The techniques fall into three broad categories:
– Image Restoration and Rectification
– Image Enhancement
– Image Classification
• Image Restoration
• Most recorded images are subject to distortion due to noise which degrades the image.
• Two of the more common errors that occur in multi-spectral imagery are striping (or banding) and line dropouts
• Striping (or banding)
• Dropped Lines are errors that occur in the sensor response and/or data recording and transmission which loses a row of pixels in the image.
Image Enhancement
• One of the strengths of image processing is that it gives us the ability to enhance the view of an area by manipulating the pixel values, thus making it easier for visual interpretation.
• Techniques which we can use to enhance an image are such as Contrast Stretching and Spatial Filtering.
• Contrast Stretching
• Contrast enhancement involves changing the original values of image pixels so that more of the available range is used, this then increases the contrast between features and their backgrounds.
• Spatial Filtering
• Spatial filters are designed to highlight or suppress features in an image based on their spatial frequency.
• The spatial frequency is related to the textural characteristics of an image
• Rapid variations in brightness levels ('roughness') reflect a high spatial frequency; 'smooth' areas with little variation in brightness level or tone are characterized by a low spatial frequency
Image Classification
• In digital images processing, two methods are used to classify an image:
• Unsupervised Classifications and Supervised Classifications.
• Unsupervised Classifications
• This is a computerized method without direction from the analyst in which pixels with similar digital numbers are grouped together into spectral classes using statistical procedures such as nearest neighbor and cluster analysis
• The resulting image may then be interpreted by comparing the clusters produced with maps, air photos, and other materials related to the image site
Supervised classification
• In this classification, the spectral features of some areas of known land cover types are extracted from the image by the analyst.
• These areas are known as the "training areas".
• Every pixel in the whole image is then classified as belonging to one of the classes depending on how close its spectral features are to the spectral features of the training areas.
True and false colour Composites/images
• To generate colour images the three grey scale images need to be displayed in the three primary colours red, green and blue.
• A combination of different proportions of the three primary colours gives the full spectrum of colours.
• When images acquired in the red, green and blue parts of the electromagnetic spectrum are displayed in red, green and blue colour, respectively, the output composite image is called a true or natural colour image.
• On a true colour image, the colours of an object are similar to the colours the human eye perceives in real life – i.e., a green tree appears green, a blue lake appears blue, etc.
• Any image acquired in any part of the EM spectrum can be coded in any colour to generate a colour composite image.
• However, the colours in the output image will vary depending on the choice of the images and the choice of the colour in which they are displayed.
• As the colours of objects on the output image are different from what a human eye perceives, these images are called false colour composites.
