Supervised Classification in Imagine D. Meyer [email protected] E. Wood [email protected].
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Transcript of Supervised Classification in Imagine D. Meyer [email protected] E. Wood [email protected].
![Page 1: Supervised Classification in Imagine D. Meyer dmeyer@usgs.gov E. Wood woodec@usgs.gov.](https://reader036.fdocuments.in/reader036/viewer/2022070415/5697bff41a28abf838cbcd16/html5/thumbnails/1.jpg)
Supervised Classification in Imagine
![Page 2: Supervised Classification in Imagine D. Meyer dmeyer@usgs.gov E. Wood woodec@usgs.gov.](https://reader036.fdocuments.in/reader036/viewer/2022070415/5697bff41a28abf838cbcd16/html5/thumbnails/2.jpg)
Concept: Supervised Classification
• The goal of this exercise is to use the spectral signatures of different land covers to create a supervised classification.
• We will attempt to map the same land cover classes covered in the last exercise.
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Geospatial data fundamentals
• Geospatial information types:– Raster: “images” composed of “pixels”– Vector: points, lines, polygons (“shapes”)
• Raster data types:– Continuous
• Single attribute (panchormatic = “black & white”)• Multiple attribute (multi-spectral = “color”)
– Discrete:• Quantized continuous• Categorical
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Continuous vs. Categorical• “Feature space” – set of all attributes
describing an object.• Student feature space:
– Height (continuous)– Weight (continuous)– Hair color (weirdly continuous)– SSN (categorical) -doesn’t make sense to take an “average” SSN
• GIS attributes– Continuous – How warm? How bright? How much photosynthesis?
What’s the mean population density? Crime rate per 100,000?
– Discrete – what type of land cover? In which country is it located?
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Categorical – Land Cover
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How to ClassifyMultispectral Images
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RGB: decomposing images
RGB red green blueClass Red Green Blue
Tomato Bright Very dark Very dark
Background Very dark Kinda dark Medium
Green pepper Kinda dark Medium Very dark
Yellow pepper Very bright Kinda bright Very dark
Orange pepper Very bright Kinda dark Very dark
Garlic Very bright Very bright Very bright
Bowl Medium Medium Medium
![Page 8: Supervised Classification in Imagine D. Meyer dmeyer@usgs.gov E. Wood woodec@usgs.gov.](https://reader036.fdocuments.in/reader036/viewer/2022070415/5697bff41a28abf838cbcd16/html5/thumbnails/8.jpg)
RGB: spectral signaturesClass Red Green Blue
Tomato Bright Very dark Very dark
Background Very dark Kinda dark Medium
Green pepper Kinda dark Medium Very dark
Yellow pepper Very bright Kinda bright Very dark
Orange pepper Very bright Kinda dark Very dark
Garlic Very bright Very bright Very bright
Bowl Medium Medium Medium
Bright
Very bright
Kinda bright
Medium
Kinda dark
Dark
Very dark
Red Green Blue
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Supervised Classification• Very widely used method of
extracting thematic information
• Use multispectral (and other) information
• Separate different land cover classes based on spectral response, texture, ….
• i.e. separability in “feature space”
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Supervised classification
• Want to separate clusters in feature space
• E.g. 2 channels of information• Are all clusters separate?
10
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Tools• Identify spectral signatures of different land
cover types using tools within Imagine: – Signature editor
• Alarm feature• Signature editor statistics
– Areas of interest (AOI’s)• AOI tool
– Supervised classifier (“maximum likelihood”)– Raster Attribute Editor
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Supervised Landsat Classification• Open “germtm.img” from the data folder (RGB=5,4,3)
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AOI tool• Open AOI -> AOI Tool• Open AOI -> create polygons around training sites
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Signature Editor• Have the Classification menu open• Utility -> inquire box and locate given x,y coordinates
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Classify the image
• The goal of this exercise is to use the spectral signatures collected in the previous to classify the reflectance image: germtm.img (open this in a viewer, r,g,b->5,4,3)
• Open the previous AOI for germtm.img from the “spectral signatures” exercise. In the viewer menu bar: File-> Open-> AOI Layer to see the training polygons.
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Input image with AOI’s
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Classify the image• In the Imagine Toolbar, click on the “
Classifier” button to get the Classifier menu; click on “Supervised Classification”
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Classify the Image• Input file: “germtm.img”• Signature file:
“germtm.sig” (from before) • Output file:
“germtm_sup.img” (in results folder for the current exercise)
• Parametric rule: Maximum Likelihood.
• Click “Okay”
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Classify the image• Open classified image in the same viewer as the input
image (deselect “clear display”)• Select the “Arrange Layers” icon in the Viewer and move
the AOI layer to the bottom to hide the polygons (“Apply”).
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Classify the image• Swipe between the input and classified image. Move around and swipe between different areas to observe the results.
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Refine the classification• From the viewer window, select Raster->Attributes
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Refine Classification• In the raster attributes editor, click column properties icon to edit the location and size of the
columns in the editor.• Move the “Class Names” column heading to the “top” and change it’s wide to 10 (makes it
leftmost column).• Move the “color” heading “up” just below “Class Names”
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Refine Classification• Make various “classes” red to evaluate it’s
accuracy (good urban classification)
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Refine Classification• Make various “classes” red to evaluate it’s
accuracy (questionable urban classification)
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Refine the classification• One solution: delete the problem class in the signature file (iterate
for all classes).• Rerun classification with updated signatures.
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Compare to Unsupervised classification
• Open “xiso.img” from the previous exercise (DO NOT CLEAR DISPLAY
• Use swipe to make a quantitiative comparison with germtm_sup.img
• Using the raster attributes editor, compute the number of pixels in each class for both the unsupervised and supervised classification