lecture 5 - app. rs

8/7/2019 Lecture 5 - App. RS

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8/2/058/2/05 Applied Remote SensingApplied Remote Sensing 11

Objective is to automatically categorized all

pixels in an image into land cover classes;

Classification normally performed with

multispectral data;

Different features have different combination ofDNs based on their spectral reflectance or

emittance properties;

The DNs are used as a numerical basis for

classification; Pattern is discerned from the set of radiance

measurement obtained in the various

wavelength bands;

Lecture 5

Image Classification


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Digital image classification uses the spectral information

represented by the digital numbers in one or more spectralbands (A), and attempts to classify each individual pixel based

on this spectral information (B).


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Identification of Patterns:

Spectral pattern recognition: Classificationusing pixel-by-pixel spectral information as the

basis for land-use classification;

Spatial pattern recognition: Categorization of

image pixels based on the spatial relationshipwith surrounding pixels e.g. using image texture,

pixel proximity, feature size, shape etc.

Temporal pattern recognition: Use time to aid

in feature identification e.g. examining spectral

and spatial changes during the growing seasonto discriminate differences in multidate imagery;


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Classification is used to provideinformation on among others things:

Land Use;

Vegetation types;

Soil, minerals and geomorphology;


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Types of Classification:

Visual interpretation:

Simple and easy to implement but very

subjective and time consuming.

Digital image classifications:

Supervised classification

- when the identity and location of land cover

types is known beforehand;

- The analysis supervises the pixel

categorization process by specifying

numerical descriptors of the various land use;


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Unsupervised classification

- when there is no or only limited beforehand

knowledge of the land cover;

- Aggregating image data into natural

spectral grouping or clusters;


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Supervised Classification: General steps

1. Training stage:

The definition of classes and the selection ofrepresentative training areas;

Training areas are regions within the imagethat are representative of the land coverclasses must be homogeneous;

2. Classification stage:

The allocation of pixels to the defined classes3. Output stage:

The accuracy of the classification is assessed


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Training Areas

Forest

Water

Urban

Clouds

Grass Agriculture


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The Training Stage:

Requires substantial reference data and throughknowledge of the area to which the data apply;

Overall objective to assembly a set of statisticsthat describes the spectral response pattern ofeach land cover;

Training sets must be both representative andcomplete i.e. statistics for all spectral classesrepresented;

- E.g. if a water body contain two distinct areasof clear and turbid water a minimum of two

spectral classes are required to train for thisfeature;

- Agricultural class may consist of several croptype each of which must be represented;


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Training areas normally established by using

enlarged windows;

Avoid pixel located on the edge between two

land cover type;

Types of analysis forTraining Set

Refinement:

I. Graphical representation of the spectral

response pattern:

The graphical display of training areas -

histograms: Provides a visible check on the normality of

the spectral response distribution;


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Statistics Extraction:

Once you training areas have been digitized

extraction of statistics for the training areas

follows;

Normal distribution is achieved by ensuring that

training data are pure that is they include only

one land cover class; If more than one land cover class is present

within the training area for a particular class the

normal distribution may be violated or;

The training data may be composed of twosubclasses with slightly different spectral

characteristics;


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Important statistics includes:

Mean;

Minimum and maximum;

Standard deviation;

Co-variance;

Correlation;

mean, standard deviation and co-variance are

only meaningful if the input data are normally

distributed;


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Separability

One way of enhancing the output of a

classification, is to examine the separability of the

defined classes;

- Coincident spectral plots using the mean

spectral response and the variance of thedistribution;

Indicate the overlap between category response

pattern;

Plot also show which combination of bands arebest for discrimination;


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II. Quantitative expression of category

separation:

Statistical separation between category

response pattern are computed for all pair of

classes, presented in the form of a divergence

matrix;

i.e. a covariance-weighted distance betweencategory means;

III. Self-classification of training set data:

Using an error matrix to determine what

percentage of the training pixels are actuallyclassified as expected;


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Supervised Classification:

Classification stage:

The assignment of unknown pixels to one of anumber of classes using a certain decision rule;

Some of the most frequently used decision rulesare the minimum distance to means, theparallelpiped classiferand the maximum

likelihooddecision rule; Minimum distance:

Based on the distance to the mean vector for eachclass;

A pixel is assigned to that class where the distance

to the mean vector is shortest; Not widely used in application where spectral

classes are close in measurement space and havehigh variance;


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Parallelepiped Classifier:

Range defined by the highest and lowest DNvalue in each band and appear as a rectangular

area (parallelepiped);

Unknown pixels are classified according to the

category range, ordecision region in which itlies;

Difficulty occur when categories overlap

classified as not sure disadvantage;


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Maximum Likelihood Classifier:

Assumes a multivariate normal distribution;

Given this assumption, the class signature can be

completely described by the:

- mean vector;

- covariance matrix;

With these two statistical values we can calculate the

probability of a pixel belonging to each class;

Based on a probability function. A pixel is assigned to

the class where it has the highest probability ofbelonging to;


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Advantages of Supervised Classification:

Control over a selected menu of informational

categories tailored to a geographic region or

specific purpose;

Tied to a specific area of known identity,

determined by selecting training sets; Not faced with the problem matching spectral

categories on final map with informational

categories;

The operator may be able to detect serious errorin classification by examining training data;


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Disadvantages and Limitations:

The analysis impose a classification structure on thedata may not match natural breaks;

Training data defined primarily with reference to

informational categories and only secondarily with

reference to spectral properties;

Training data may not be representative of condition

throughout the image;

Pure selection of training data may be time-

consuming, expensive, and tedious;

Special or unique categories not represented in the

training date may not be recognized not known or

occupy a very small area of the image;

lecture 5 - app. rs

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