Lecture 6R - App. RS

8/7/2019 Lecture 6R - App. RS

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Unsupervised Classification: In many cases you dont have the necessary

beforehand knowledge of the land cover needed toperform a supervised classification in which case anunsupervised classification can be used.

- the definition, identification, labeling, andmapping of natural classes with respect to brightnessin several spectral channels;

General steps Automatic grouping of pixels into similar spectral

classes (e.g. the ISODATA clustering algorithm). Label spectral classes into information classes (i.e.Land cover classes).

Perform accuracy assessment

Lecture 6

Image Classification


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Classification involving algorithms that examine the

unknown pixels in an image; Pixels then aggregated into a number of classes based on

the natural groupings or clusters present in the imagevalues;

Values within a given cover type should be close togetherin the measurement space, whereas data in different classesshould be comparatively well separated - basic premise ;

The resulting classes are spectral classes; Initial identification of spectral classes are not known

because they are based sole on natural grouping;


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Analysis must compare classified data with somereference data e.g. large scale images or maps;

In supervisedclassification useful informationcategories are first defined then examination oftheir separability is done;

In unsupervisedclassification separability is firstdetermined then definition of their informationalutility;


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Approaches to Unsupervised Classification:

Classification Algorithm: Distance measurement:

Classification of an entire image must considerthousands of pixels;

To find grouping of pixels, the distance between

pairs of pixels are calculated; The simplest method is Euclidean distance based

on Pythagorean theoremc = a2 + b2

Finding the distance a, b, and c which are measured

in units of the two spectral channels;


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In relation to other distancesthis form a means ofdefining similarities betweenpixels;

For example, if the distanceab = 40.5 and ac = 86.3 thewe know A is closer andtherefore similar to B than itis to C;

The group should be formed

between A and B rather thanA and C

The measure can be applied to

as many dimensions (spectral

channels as might be available,by addition of distances e.g.

above


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Thousands of distance

calculations made as a

means of determining

similarities; Proceeds in an interactive

fashion to search for an

optimal allocation of pixel

categories, given the

constraints specified bythe analysis;


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K-means Approach:

The number of clusters are set by the analyst location

of that number of cluster centers in multidimensionalspace is then calculated; Pixel are assigned to the cluster whose arbitrary mean

vector is closest; After assigning pixels to a cluster the mean vectors for

each clusters are revised and computed; Revised means used as a basis to reclassify the image

minimization of within group variation and maximizingbetween group variation;

This procedure is continued until there is no significant

changes in the location of class mean vectors betweensuccessive iterations as the classes meet all theconstraints required by the operator;


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At this point the analyst determines the land coverfor each spectral class;

Often applied to sub-areas rather than full scenes;

Training statistics developed for combiningclasses are used to classify the entire scene (e.g.minimum distance or maximum likehood

algorithm) called hybrid classification usingboth supervised and unsupervised techniques;

Hybrid classification helps when there arecomplex variability in spectral response due to

variation in species (i.e. cover type) and differentsite condition (soils, slope, aspect);


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Accuracy Assessment:

A classification is not finished before the accuracy ofthe map has been assessed;

Visually

A simple visual inspection does the map look

like the real world;Confusion matrix (Error Matrix)

A table that shows how individual classes isclassified in relation to a set of reference data

(test data);


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Sources ofClassification Error:

Classification error is the assignment of a pixelbelonging to one category to another category duringthe classification process

The simplest causes of error are related tomisassignment of information categories to spectral

categories; Mixed pixels occur as resolution elements fall on the

boundaries between landscape parcels;

Compiling the Error Matrix:

Comparison of two images the reference image andthe image to be evaluated on a point-by-point basis;


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Determination of how each site on the reference imageis represented in the classification;

Comparison between image and a map depicting fieldverified data;

A network of uniform cells will form the units ofcomparison, small enough to provide enough cell for

statistically valid samples and avoiding mixed cells; Images are superimposed for compilation manually or

digitally using a computer;

If differences are due to one being more detailed, the

more detailed map can be collapsed into more generalclasses


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Overall Accuracy:

The simple expression is:

The number of correctly classified pixels inrelation to the total number of pixels;

Should be in the order of at least 70-80%

A good classification has an overall accuracygreater than 85%


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Producers Accuracy:

The Producer is you;

Interested in how well a certain class is classified;

Therefore the producer wants to know how manyreference pixels for a certain class that has beencorrectly classified;


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Users Accuracy:

The user is the final reader of the map;

Interested in how well a certain class on the map

actually represent that class on the ground; Therefore the user wants to know how many

reference pixels for a certain class has beencorrectly classified;

- a guide to the reliability of the map as a predictivetool;


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The Confusion Matrix


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Advantages ofUnsupervisedClassification:

No extensive prior knowledge of the region is

required: Opportunity for human error is minimized:

Unique classes are recognized as distinct units:

Disadvantages andLimitations:

May identify spectrally homogeneous classes in thedata that does not necessarily correspond to theinformation of interest to the analysis;

Limited control over the menu of classes and theirspecific identity;

Spectral properties of specific informational classeswill change over time;


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FieldData:

Defining the relationship between image data and

corresponding points on the ground; Acquiring data suitable for specific tasks and

establishing with confidence the relationship betweenthe image and condition on the ground;

Kinds ofFieldData:

Field data serves one of three purposes;i. To verify, evaluate or assess the results of RS

investigation;ii. Provision of reliable data to guide the

analytical process e.g. creating training fieldto support supervised classification;


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iii. Provision of information used to modelspectral behavior of specific landscape

features e.g. plants, soil, water bodies; Field data must contain at least three kinds of

information;i. attributes or measurements that describe

ground conditions at a specific placee.g. identification of specific crop or landuse;

ii. Observations must be linked to locationand size e.g. slope, aspect, and

elevation;- matching attributes to correspondingpoints in the image;


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iii. Observation must be describe withrespect to time and date;

Complete field data also includes other data e.g.weather, illumination, calibration information for

instruments etc. The purpose of field data is to facilitate

reconstruction in as much detail as possible, ofground and atmospheric conditions at the time andplace the image was taken;


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Biophysicaldata:

Measurement of physical characteristics collected inthe field e.g. type, size, form, spacing of plants etc.;

Specific to the purpose of the study typicallyinclude characteristics such as, leaf area index (LAI),biomass, soil texture, and soil moisture;

Measurements vary over time therefore carefulrecords of time, date, location, and weathercondition is necessary;

Lecture 6R - App. RS

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