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Classification

= Semi-automated grouping of similar pixels using their multispectral DNs

Remote Sensing Classification Automated grouping of similar pixels using multispectral DNs

Developed since 1972 (Landsat 1)

Digital alternative to manual mapping

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Remote sensing Classification

Semi-automated grouping of similar pixels using their multispectral DNs Slide Number

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Manual interpretation e.g. air photos

Features are classified = simplified

Human interpretation / classification relies on attributes such as: Shape, pattern, texture, shadows, size, association, tone, colour

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Using just one band to classify ?

A band (or colour composite) could be treated as an air photo (interpretation) Digital Numbers from one band alone are rarely enough – features are not unique

Band 3 Band 4

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The role of multispectral sensing in classification use of multiple bands – 2 features similar in one band, differ in another

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The role of multispectral sensing in classification

DN Band A

DN band B

DNs in Band A are similar for Corn and Wheat DNs in Band B are similar for Corn and Soybeans … but if we use both Bands A and B, then ……

http://fas.org/irp/imint/docs/rst/Sect1/Sect1_16.html

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Classification – land cover

Image classification uses multispectral digital numbers (‘colour’) Algorithms are ‘per pixel’ classifiers

http://fas.org/irp/imint/docs/rst/Sect1/Sect1_16.html

3 bands … 3D (and we have 7 bands)

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Band / channel selection

Landsat TM has 7 bands: You would NOT select 3 visible bands to classify The visible bands are similar – and thus the composite is low in contrast

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sample band correlation coefficients

Example: PG Landsat data (r values between bands)

TM1 2 3 4 5 6 TM1 TM2 .97 TM3 .96 .96 TM4 .07 .16 .11 TM5 .66 .72 .76 .46 TM6 .77 .77 .81 .14 .80 TM7 .83 .86 .90 .25 .93 .86

The Visible bands are highly correlated (similar) .. (r = .96 to .97)

.. so are bands 5 and 7 (r = .93)

band 4 (near-IR) is not very correlated with Visible or MIR (nor thermal)

Note: these values will vary for different environments e.g. urban, desert, forested

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Classification: Band / Channel Selection

How to choose which ones to use: 1. Low correlation e.g. TM 3-4-5 or 2-4-7 (Visible-NIR-MIR) (example below shows high ‘r’ Red v Green and lower ‘r’ Red v NIR)

2. Past experience, visual examination, logical thinking 3. Channels that separate the features we want to identify (based on DNs / spectral curves)

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A> Unsupervised classification

Characteristics

-user needs no 'a priori' knowledge of area - software clusters pixels by natural DN groupings (based on similarity and contrast – ‘natural breaks’) --------------------------------------------------------------------------- Steps - determine input bands / channels and - determine how many classes

- run classifier : K-means or Isodata

-assign names to classes (merge classes if needed)

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Unsupervised result – 10 classes (clusters)

K-Means and Isodata: K-means minimises within cluster range Isodata can also merge / split clusters

Unsupervised – how it works Algorithm starts with statistical seed points

Assigns each pixel to the closest seed

Calculates group mean

Re-assigns pixels to the closest group mean

Re-calculates group mean

Iterates (10?) until relatively little change and fixes groupings

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Iterations in unsupervised classification

Final step .. Assigning names to clusters (and merge some)

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PG classification report 1 iteration

DN values for bands 3,4,5

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After 16 iterations

Unsupervised classification

Input bands selected – minimum 3 or 4 bands; more acceptable

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Merging and adding classes

Merging – clusters are not really separate features Clusters can be merged if they overlap spatially or are not distinguishable spectrally.

Splitting / adding If one cluster covers too much area – run again with more clusters Can generate many clusters, and then group merge later

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Iskut 543 – Water /ice

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K-means

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Fuzzy K-means

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Isodata

Mt. Edziza – classification and sieve (‘peneira’)

- recognises connectivity of adjacent pixels in the same class - special classes e.g. wetlands can be specified and preserved - removes small sub-areas; does not ‘blur’ edges like filtering

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Sieve - filter

Classification ALWAYS produces a 'salt and pepper' effect with isolated pixels This is a result of a. the local variations in DNs and b. using ‘per-pixel’ classifiers

Mt. Kilimanjaro

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Vector conversion After classes are finalised, the adjacent class pixels can be transformed into vector polygons, using polygon growing or raster to vector options (see feature extraction and lab 7)

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Thursday lecture and Lab next week :

supervised classification

Monday 6pm: wee outing

“pequena turnê”

(weather permitting)

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Challenges in classification There are many causes of spatial variations in reflectance (a range of DNs for a feature) NATURAL RESOURCES (e.g. forest stands) purity of stand, understory, age/maturity, density, health/disease, sun angle, topography

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There are many causes of spatial variations in reflectance (a range of DNs for a feature) URBAN / HUMAN (e.g. roads or residences) - amount of grass, types of material, roofing colour, weathering, sun angle (building shape)

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There are many causes of spatial variations in reflectance (a range of DNs for a feature)

All imagery: moisture, edge (mixed) pixels, sun angle, time of year