LECTURE SLIDES

1. 1
SOKOINE UNIVERSITY OF AGRICULTURE
FACULTY OF AGRICULTURE
DEPARTMENT OF AGRICULTURAL
ENGINNERING AND LAND PLANNING
Course LRM 111: Introduction to Remote
Sensing
LESSON NOTES-VII: Image Interpretation and
classification

2. 2
Image Interpretation and classification
PurposePurpose
•To extract meaningful information from remote sensing dataTo extract meaningful information from remote sensing data
•To make information contained in remote sensing data usableTo make information contained in remote sensing data usable
•To derive useful spatial information for use in conjuction with otherTo derive useful spatial information for use in conjuction with other
geographical datageographical data
Methods used to extract information from remote sensing dataMethods used to extract information from remote sensing data
•Visual interpretationVisual interpretation
•Digital image classificationDigital image classification

3. 3
Visual interpretationVisual interpretation
•Takes adavantages of the human skils to recognise information contained onTakes adavantages of the human skils to recognise information contained on
the imagethe image
•Use combination of several image interpretation key characteristics (size,Use combination of several image interpretation key characteristics (size,
colour tone, texture, shape, pettern etc)colour tone, texture, shape, pettern etc)
•Visual interpretation is done using analog imagery displayed in pictorial or inVisual interpretation is done using analog imagery displayed in pictorial or in
digital format as hard copydigital format as hard copy or on a computer screenon a computer screen
•They can be displayed as black and white called monochrome images or asThey can be displayed as black and white called monochrome images or as
colour compositecolour composite
Basic principles of image interpretation
Image parameters
•Source of data (from which sensor, band)
•Band combinations used to make colour composite

4. 4
•Enhancement techniques
•Date of image acquisition – with respect to season, crop calendar
•Knowledge on the type of terrain to be expected
Spectral reflectance characteristics of various terrain features
Knowledge of colour theory
Image key characteristics
Interpretation follows pronciples and procedures as outlined in the notesInterpretation follows pronciples and procedures as outlined in the notes
on principles or techniques of aerialphoto-interpretation (detection,on principles or techniques of aerialphoto-interpretation (detection,
recognition, identification, analysis, classification and deduction)recognition, identification, analysis, classification and deduction)
It is normally done on an image printed as hard copy or displayed on theIt is normally done on an image printed as hard copy or displayed on the
computer screen; the procedure known as on-screen digitisationcomputer screen; the procedure known as on-screen digitisation
Also done on an overlay fixed on an image hard copyAlso done on an overlay fixed on an image hard copy

5. 5
Digital image classification
Use computer classification algorithms to classify image pixels based on
their brightness values
Referred to as quantitative analysis or digital image classification
Digital image Classification is the process ofDigital image Classification is the process of sorting pixelssorting pixels into a finiteinto a finite
number ofnumber of individual classesindividual classes, or categories of data, based on their data file, or categories of data, based on their data file
valuesvalues
If a pixel satisfies a certain set of criteria, then the pixel is assigned toIf a pixel satisfies a certain set of criteria, then the pixel is assigned to
the class that corresponds to that criteriathe class that corresponds to that criteria
For the first part of the classification process, the computer system mustFor the first part of the classification process, the computer system must
bebe trainedtrained to recognize patterns in the datato recognize patterns in the data
Training is the process of defining the criteria by which these patternsTraining is the process of defining the criteria by which these patterns
are recognisedare recognised
The result of training is a set ofThe result of training is a set of signaturessignatures, which are criteria for a set of, which are criteria for a set of
proposed classesproposed classes

6. 6
There are two ways to classify pixels into different categories:There are two ways to classify pixels into different categories:
• Supervised classification
• Unsupervised classification
Supervised classification
There are three steps involved in a typical supervised classification
procedure
Training stage
The analyst identifies representative training areas and develops a
numerical description of the spectral attributes of each land cover type of
interest on the image

7. 7
Classification stage
Each pixel in the image data set is categorised into the land cover class it
most closely resembles
If the pixel is insufficiently similar to any training data set it is usually
labelled “unknown”
The category label assigned to each pixel in this processes is then
recorded in the corresponding cell of an interpreted data set (an output
image)
Output stage
After the entire data has been categorised, the results are presented in
the output stage which may be used in a different number of ways

8. 8
Three typical form of output products are:
Thematic maps
Tables of area data or statistics of various land cover classes
Digital data files which can transformed for inclusion in a
Geographic Information System (GIS)
Thus the supervised classification defines useful information categories
and then determine their spectral separability

9. 9
Selected MSS measurements made along one scanline: channels cover
the following spectral bands: 1-blue, 2-green, 3-red, 4-near-infrared, 5-
thermal infrared

10. 10
Basic steps in supervised classification

11. 11
Unsupervised classification
It is always a good reference to first perform an UnsupervisedIt is always a good reference to first perform an Unsupervised
classificationclassification
This gives a general impression of the classes involvedThis gives a general impression of the classes involved
Unsupervised classification does not utilise training data as the basis for
classification
It involves algorithms that examine the unknown pixels in an image and
aggregate them into a number of classes based on natural groupings or
clusters present in the image values
That is: it identifies the distinct spectral classes present in the image
Aggregate the identified spectral classes based on natural
groupings/clusters of the image pixel values
Then the statistical properties of the spectral reflective values of the
image are used as a basis for classification

12. 12
In unsupervised classification the analyst must compare the established
spectral classes (classification results) with reference data or ground truth
to transform them into information classes
In the unsupervised classification the analyst determine spectrally
separable classes and then define there information utility