Image Interpretation

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Image Interpretation

• Proficiency based on knowledge
• Subject
• Requires familiarity with subject
• Interpreter needs broad knowledge of subject and
  their interrelationships
• Geographic region
• Locality has unique characteristics influencing
  patterns
• Remote sensing system
• How image formed sensor characteristics

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Image InterpretationTasks

• Classification
• Assign to classes based on appearance
• 3 levels of confidence and precision
• 1. Detection
• 2. Recognition
• 3. Identification
• Enumeration
• Counting visible items

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Image InterpretationTasks

• Measurement or mensuration
• 2 kinds
• measurement of distance height
• and image brightness
• Delineation
• Interpreter must separate distinct aerial units
  identify edges or boundaries
• Typical problems
• selecting appropriate level of generalization and
  boundary placement where gradation exist

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BASIC INTERPRETATIONCHARACTERISTICS

• Shape general form, configuration, or outline
• Association occurrence of objects in relation
  to others
• Size consider objects on photo in context of
  photo scale
• Size is relative in relation to other objects on
  the image gives interpreter intuitive notion of
  scale and resolution of image
• Absolute measurement can be as valuable as
  interpretation

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BASIC INTERPRETATIONCHARACTERISTICS

• Pattern spatial arrangement of objects into
  distinctive, recurring forms

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BASIC INTERPRETATIONCHARACTERISTICS

• Image tone (or hue) relative brightness or
  color of objects on photo
• w/o tone, shapes, patterns texture cannot be
  discerned
• Influenced by intensity and angle of
  illumination and film processing

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BASIC INTERPRETATIONCHARACTERISTICS

• Texture Frequency of tonal change on image
• Aggregation of features too small to be discerned
  individually, i.e., tree leaves
• Product of individual shape, size, pattern,
  shadow, tone
• Determines overall visual smoothness or
  coarseness
• Reducing scale texture becomes finer
• Depends on surface angle of illumination
• Depends on favorable image contrast

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BASIC INTERPRETATIONCHARACTERISTICS

• Texture
• rough
• Mature forest vs.
• Smooth
• mature wheat field

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BASIC INTERPRETATIONCHARACTERISTICS

• Shadow
• 1. Affords impression of profile view
• 2. Objects within shadow reflect little light and
  difficult to discern
• Can enhance boundary between two different land
  types

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BASIC INTERPRETATIONCHARACTERISTICS

• Site
• Topographic or geographic location
• Particularly useful as aid in identification of
  vegetation types

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IMAGE INTERPRETATIONSTRATEGIES

• Field observations
• Required when interpreter cannot make accurate
  identification
• Routine as a check on accuracy or to familiarize
  the interpreter of the specific region
• Direct recognition
• Interpreters experience, skill judgment
• Process essentially a qualitative, subjective
  analysis

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IMAGE INTERPRETATIONSTRATEGIES

• Interpretation by inference
• Use of a visible distribution to map one that is
  not itself visible on the image.
• Visible distribution acts as surrogate or proxy
  for the mapped distribution
• Requires complete knowledge of link between proxy
  and mapped distribution

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IMAGE INTERPRETATIONSTRATEGIES

• Probabilistic interpretation
• Integrating non-image information into
  classification
• Knowledge often expressed as a statement of
  probability
• Deterministic interpretation
• Most rigorous and precise method
• Based on quantitative relationships tying image
  to ground

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COLLATERAL INFORMATION

• Non-image information used to assist interpreter
• All image interpretation use implicit collateral
  information
• Everyday experiences formal training
• Desirable to use provided
• Use of such information is to be explicitly
  acknowledged in the written report
• Must not focus on a single portion of the image
  to extent that it produces uneven detail or
  accuracy in final map

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IMAGE INTERPRETATIONKEYS

• Valuable training aids for novice
• Useful reference for experienced interpreters
• 2 basic parts
• Collection of annotated or captioned
  stereograms illustrative of features or
  conditions to be identified
• Graphic or word description

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TYPES OF INTERPRETATIONKEYS

• Selective Key
• Contains numerous photo examples
• Select the example that most nearly resembles the
  feature or condition on the photo
• Elimination Key
• Step-by-step from general to specific leading to
  elimination of features
• Usually in form of dichotomous keyswhere
  interpreters pick between two alternatives

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Interpretation of Digital Imagery
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Image Enhancement

• Enhancements are used to make it easier for
  visual interpretation and understanding of
  imagery.
• The advantage of digital imagery is that it
  allows us to manipulate the digital pixel values
  in an image
• There are three main types of image enhancement
• Contrast enhancement
• Spatial feature enhancement
• Multi-image enhancement

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Contrast Enhancement- Image Histogram

• In raw imagery, the useful data often populates
  only a small portion of the available range of
  digital values (commonly 8 bits or 256 levels).
• Contrast enhancement involves changing the
  original values so that more of the available
  range is used, thereby increasing the contrast
  between targets and their backgrounds.
• The key to understanding contrast enhancements is
  to understand the concept of an image histogram

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Contrast Enhancement- Image Histogram

• A histogram is a graphical representation of the
  brightness values that comprise an image.
• The brightness values (i.e. 0-255) are displayed
  along the x-axis of the graph.
• The frequency of occurrence of each of these
  values in the image is shown on the y-axis.

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Histogram Stretch

• By manipulating the range of digital values in an
  image, graphically represented by its histogram,
  we can apply various enhancements to the data.
• There are many different techniques and methods
  of enhancing contrast and detail in an image we
  will cover only a few common ones here.
• Linear Stretch
• Histogram Equalised Stretch

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A linear stretch involves identifying lower and
upper bounds from the histogram (usually the
minimum and maximum brightness values in the
image) and applying a transformation to stretch
this range to fill the full range.
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After stretch
Before stretch
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In this case, a histogram-equalised stretch may
be better. This stretch assigns more display
values (range) to the frequently occurring
portions of the histogram.
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Before
After
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A common filtering involves moving a 'window' of
a few pixels in dimension (e.g. 3x3, 5x5, etc.)
over each pixel in the image, applying a
mathematical calculation using the pixel values
under that window, and replacing the central
pixel with the new value.
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Spatial Filtering

• Spatial filtering encompasses another set of
  digital processing functions which are used to
  enhance the appearance of an image.
• Spatial filters are designed to highlight or
  suppress specific features in an image based on
  their spatial frequency.
• Spatial frequency is related to the concept of
  image texture, and refers to the frequency of the
  variations in tone that appear in an image

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A low-pass filter is designed to emphasise
larger, homogeneous areas of similar tone and
reduce the smaller detail in an image. Thus,
low-pass filters generally serve to smooth the
appearance of an image.
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A high-pass filter does the opposite, and serves
to sharpen the appearance of fine detail in an
image.
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Vertical edges
Horizontal edges
Directional or edge detecting filters highlight
linear features, such as roads or field
boundaries. Also designed to enhance features
which are oriented in specific directions and are
useful in applications such as geology, for the
detection of linear geologic structures.