Image Classification Techniques
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Image Classification Techniques

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Questions and Answers

Which characteristic is NOT an element of image interpretation?

  • Shadow
  • Shape
  • Color
  • Density (correct)

    • Spatial resolution refers to the largest possible feature that a sensor can detect.

    False

    Name one factor that increases challenges for remote sensing imagery interpretation.

    Unfamiliar aerial perspective

    The spatial resolution of a sensor is limited by its ______ size.

    <p>pixel</p> Signup and view all the answers

    Match the following terms with their definitions:

    <p>Spatial resolution = Ability to identify smallest features Spectral signatures = Unique reflection/emission characteristics of objects Image classification = Organizing pixels into meaningful categories Supervised classification = User-defined training for classifying imagery</p> Signup and view all the answers

    What is a key requirement for supervised classification?

    <p>Analyst defines and selects samples</p> Signup and view all the answers

    Unsupervised classification allows for the analyst to have complete control over class definitions.

    <p>False</p> Signup and view all the answers

    What is the primary advantage of using supervised classification?

    <p>The analyst can control the selected classes based on known identity.</p> Signup and view all the answers

    In supervised classification, the purpose of using __________ is to recognize spectrally similar areas for each class.

    <p>spectral information</p> Signup and view all the answers

    Which of the following is a disadvantage of supervised classification?

    <p>Unique classes may not be recognized</p> Signup and view all the answers

    Match the following aspects with their classification types:

    <p>Controlled by analyst = Supervised Classification Recognizes unique classes = Unsupervised Classification Requires ancillary data = Supervised Classification Minimized human error = Unsupervised Classification</p> Signup and view all the answers

    What is the main goal of accuracy assessment in classification?

    <p>To measure how well a classification worked by comparing classified maps with reference data.</p> Signup and view all the answers

    Supervised classification typically requires field work to verify class identification.

    <p>True</p> Signup and view all the answers

    Which of the following satellite sensors has a 10-meter resolution?

    <p>MODIS</p> Signup and view all the answers

    Stressed plants reflect more in the NIR compared to healthy plants.

    <p>False</p> Signup and view all the answers

    What is the revisit cycle for the Landsat satellite?

    <p>16 days</p> Signup and view all the answers

    ___ classification involves grouping similar pixels together based on their attributes.

    <p>Unsupervised</p> Signup and view all the answers

    Match the following satellite sensors with their respective properties:

    <p>Sentinel = Free of charge, 30-m resolution, Images since 1984 Landsat = Free of charge, 10-m resolution, Images since 2014 MODIS = Free of charge, 250-m resolution, Images since 2000 QuickBird = Charged, 0.6-m resolution, Images since 2001</p> Signup and view all the answers

    Which factor does NOT influence NDVI readings?

    <p>Leaf area index</p> Signup and view all the answers

    Derivatives of NDVI such as LAI are physical quantities that can be used reliably.

    <p>False</p> Signup and view all the answers

    What is the primary function of image classification in remote sensing?

    <p>To turn remotely sensed data into meaningful categories representing surface features.</p> Signup and view all the answers

    Study Notes

    Supervised Classification

    • Analyst defines and selects samples to classify image based on chosen samples
    • Training data is used to 'train' the algorithm to recognize spectrally similar areas for each class
    • Algorithm determines numerical ‘signatures’ for each training class
    • Each pixel is compared to class signatures and labelled as the class it most closely resembles
    • Requires ancillary data (maps, photos, etc)
    • Field work is often needed to verify
    • Analyst has control over the selected classes, tailored to the purpose
    • Disadvantage: Analyst imposes a classification that may not be natural.

    Unsupervised Classification

    • Requires no prior knowledge of the region
    • Human error is minimized
    • Unique classes are recognized as distinct units
    • Disadvantage: Classes may not necessarily match informational categories of interest

    Accuracy Assessment

    • Measure of how well a classification worked
    • Determination of class types at specific locations
    • Compare reference to classified map
    • Indicators must be sufficiently representative and easy to understand and measure on a routine basis

    Vegetation Spectral Reflectance Curves

    • Healthy plants reflect highly in the NIR
    • Stressed plants reflect less in NIR
    • Healthy plants absorb well in the SWIR regions
    • Stressed plants absorb less in the SWIR

    Spectral Indices

    • NDVI is an example of a spectral index
    • NDVI saturates over dense vegetation
    • Any factor that unevenly influence the red and NIR reflectance will influence the NDVI

    Satellite Sensors and their properties

    • Landsat images are free of charge and have a 16-day revisit cycle
    • Sentinel images are free of charge with a 12-day revisit cycle
    • MODIS images have a 1-2 day revisit cycle and are free of charge
    • Worldview-2, IKONOS, and Quickbird are all charge-based services

    Image Classification

    • Science of turning remotely sensed data into meaningful categories representing surface features or classes
    • Assigning pixels to classes
    • Group similar pixels together
    • Classifications are divided into two: Terrestrial and Non-Terrestrial

    Visual Image Interpretation

    • We make sense of what we see by interpreting a large variety of elements in visual scenes
    • Remote sensing imagery has increased challenges due to an unfamiliar perspective, the use of wavelengths outside the visual spectrum, unfamiliar scales and resolutions
    • Elements include: Shape, size, pattern, shadow, and texture

    Sensor Characteristics

    • The ability of the sensor to identify features depends on 4 types of resolutions: spatial, spectral, temporal, and radiometric
    • Spatial resolution refers to the size of the smallest possible feature that can be detected.

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    Related Documents

    ENVS211W - Introduction to Remote Sensing PDF

    Description

    Explore the concepts of supervised and unsupervised classification in image processing. Understand how algorithms utilize training data to classify images and the importance of accuracy assessment in ensuring classification success. This quiz will test your knowledge on analytical methods and their implications.

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