supervised classification in imagine

Supervised Classification in Imagine

D. Meyerdmeyer@usgs.gov

E. Woodwoodec@usgs.gov

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.

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

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?

Categorical – Land Cover

How to ClassifyMultispectral Images

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

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

BrightVery bright

Kinda brightMedium

Kinda darkDark

Very darkRed Green Blue

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”

Supervised classification

• Want to separate clusters in feature space

• E.g. 2 channels of information• Are all clusters separate?

10

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

Supervised Landsat Classification• Open “germtm.img” from the data folder (RGB=5,4,3)

AOI tool• Open AOI -> AOI Tool• Open AOI -> create polygons around training sites

Signature Editor• Have the Classification menu open• Utility -> inquire box and locate given x,y coordinates

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.

Input image with AOI’s

Classify the image• In the Imagine Toolbar, click on the “ Classifier”

button to get the Classifier menu; click on “Supervised Classification”

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”

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”).

Classify the image• Swipe between the input and classified image. Move around and swipe between different areas to observe the results.

Refine the classification• From the viewer window, select Raster->Attributes

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”

Refine Classification• Make various “classes” red to evaluate it’s

accuracy (good urban classification)

Refine Classification• Make various “classes” red to evaluate it’s

accuracy (questionable urban classification)

Refine the classification• One solution: delete the problem class in the signature file (iterate for

all classes).• Rerun classification with updated signatures.

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