Image Interpretation Dichotomous Keys - Charles Darwin...

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ENV202/502 – Introductory Remote Sensing Wk 5

Dr Karen Joyce

School of Environmental and Life Sciences

Bldg Purple 12.3.091

Lecture 5 – How is Imagery Analysed?


Your Job

• Join a ‘topic table’

– Mangroves (Malcolm, Claudia, Jack)

– Water quality / coastal (Nuzhat, Patrice, Laura)

– Natural Hazards (Valentina, Emma)

– Fire (David, Robin, Jess, Jenni, Barb)

– Deforestation / wildlife habitat (Alfredo, Yunnie, Elda, Annie)

– Other Vegetation / agriculture (Xin, Herman, Stephanie,

Andrew, Rachel)

• Visit Padlet to select a scoop topic from one of your peers

http://padlet.com/wall/env202-502-scoops

• Review their scoops and post your reactions, and / or suggest

new scoops for them

• Rescoop to your page if you like

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Image Interpretation

• Systematic approach from general to specific interpretation

• Interpretation involves both:

– Detection of a feature

– Identification of a feature

• Automated photo and image interpretation now uses this technique

Source: S.Phinn ENV202/502 – Introductory Remote Sensing Wk 54

Dichotomous Keys


Interpretation and Class Aggregation


Classification Guidelines (from USGS LU/LC

mapping)

• Overall accuracy >= 85%

• Individual category accuracy should be about equal

• Repeatable results between interpreter and over time

• Applicable over extensive areas

• Suitable for use with data obtained at different times of the year

• Categories should be divisible into more detailed sub categories

• Aggregation of categories must be possible

• Comparison with future date should be possible

• Multiple uses of a category should be recognised where possible

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Minimum Mapping Units

• Smallest size entity to be mapped as a discrete feature

• Small MMU = high detail

• Large MMU = low detail

• Small MMU can be a step in the hierarchy of a large

MMU – e.g. river, lake, stream, creek, can be grouped together to the larger MMU of water body


Interpretation CuesCue Terminology Example

Tone /

Colour

Dark, light, bright,

dull

Dark Blue (water)

Texture Smooth, rough Rough (urban area); Smooth (grass) –

function of scale

Shape Rectangular,

eliptical, regular,

irregular

Rectangular (crops)

Size Relative or

absolute

Small / large or 200x100m

Pattern Regular, random,

gridlike

Repeating linear rows (vineyard)

Shadow Presence,

absence

Long shadows observed to the SW of the

feature (can indicate height of object, time of

day, southern hemisphere). Influences tone

and texture

Site /

Association

Description of

spatial

relationships

Large carpark beside large building (may

indicate shopping centre rather than factory)


Analysing Imagery – Your Job

1. Lucy, Xin, Malcolm, Jack

2. Parash, Claudia, Robin, Yunnie

3. Stephanie, Linda, Jessica, Barbara

4. Emma, Nuzhat,

Elda, David

5. Patrice, Luis,

Valentina, Herman


Analysing Imagery – Your Job

• Identify as many classes as you can in your image

• Identify their interpretation cues

• Identify the most important or unique cue /

cue combo

• Develop a classification

key including categories

and sub categories -http://mindmapfree.com/


Classification Example

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Worldview 2 Image

Non Water Water

LandInter-tidal

Trees Grasses Artificial Surfaces

Housing Commercial /

Industrial

Roads Jetty WaterInter-tidal Trees Grasses

Dark or bright?

Small, urban pattern

Assoc. trees,

grass

Not greenGreenCircular, rough,

shadows

Irregular shape, smooth

Large, Assoc. main roads

Long, thin

Surrounded by water

Btn land and water


Classification Example - Mindmapping

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Student Choice – What’s the Point of

Hyperspectral Imagery?

13ENV202/502 – Introductory Remote Sensing Wk 5

Student Choice – What’s the Point of

Hyperspectral Imagery?

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Student Choice – How Can We Compare

Sensors with Different Spectral Sensitivities?

15ENV202/502 – Introductory Remote Sensing Wk 5



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Image Interpretation Dichotomous Keys - Charles Darwin...

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