Dealing with image data
Revision as of 08:19, 3 January 2017 by Kevin Dunn (talk | contribs)
Class date(s): | 04 November 2011 | ||||
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Class 9: Dealing with image data for product appearance monitoring (04 November)
<pdfreflow> class_date = 04 November 2011 [19 Mb] button_label = Create my projector slides! show_page_layout = 1 show_frame_option = 1 pdf_file = lvm-class-9.pdf </pdfreflow> or you may download the class slides directly.
MIA exercise
Background
If you have MATLAB, please download and install MACCMIA for to complete the questions below.
- Data file: a 7-channel Landsat image of Washington D.C. and the corresponding Google map.
- The image is a 1024 x 1026 x 7 cube, using 7.3 Mb of storage
- Channel 1 = blue/green [450 to 520 nm]
- Channel 2 = mainly green [520 to 600 nm]
- Channel 3 = red [630 to 690 nm]
- Channel 4 = Near infrared [760 to 900 nm]
- Channel 5 = Moisture and vegetation sensitive mid infrared [1550 to 1750 nm]
- Channel 6 = Thermal infrared [10400 to 12500 nm]
- Channel 7 = Mineral-sensitive mid infrared [2085 to 2350 nm]
Questions
- Identify some water pixels from the Potomac river in the score plot. Where are they located?
- Mask these water pixels to highlight the entire river. Notice how you have to adjust the mask to get both the shallow and the deep water pixels.
- Notice how the pixels from half of the Georgetown Reservoir show up with the Potomac River's pixels, and the other half show up entirely different (sediment has settled out).
- Continue investigating features from the Google map and try to locate them in the satellite map and score space. For example:
- The Reflecting Pool
- Forests in The National Arboretum
- The network of roads and highways