Image Classification

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Image ClassificationImage Classification

Automatically categorize all pixels in an image into

land use/cover classes or themes.

A process of thematic information extraction

A process of pattern recognition

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Image Classification• The process of arranging raw data DNs into information classes.

• Two Basic Types

• Supervised

• Unsupervised

Raw Imagery Extracted Information

Data Information

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Supervised Classification Supervised Classification

•The image analyst “supervises’ the pixel categorization process specifying, to the computer algorithm, numerical descriptors of the various land cover types present in a scene.

•Representative sample sites of known cover type, called training areas, are used to compile a numerical “interpretation key” that describes the spectral attributes for feature type of interest.

•The image analyst “supervises’ the pixel categorization process specifying, to the computer algorithm, numerical descriptors of the various land cover types present in a scene.

•Representative sample sites of known cover type, called training areas, are used to compile a numerical “interpretation key” that describes the spectral attributes for feature type of interest.

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Unsupervised Classification Unsupervised Classification

In an unsupervised classification, the computer groups pixels with similar spectral characteristics into unique clusters according to some statistically determined criteria.

The analyst then labels and combines the spectral clusters into information classes.

In an unsupervised classification, the computer groups pixels with similar spectral characteristics into unique clusters according to some statistically determined criteria.

The analyst then labels and combines the spectral clusters into information classes.

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Land-use and Land-cover Classification SchemesLand-use and Land-cover Classification Schemes

Land cover refers to the type of material present on the landscape (e.g., water, sand, crops, forest, wetland). Land use refers to what people do on the land surface (e.g., agriculture, commerce, settlement).

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A classification scheme contains taxonomically

correct definitions of classes of information that are

organized according to logical criteria.

The classes in the classification system should

normally be:

• mutually exclusive,

• exhaustive, and

• hierarchical.

A classification scheme contains taxonomically

correct definitions of classes of information that are

organized according to logical criteria.

The classes in the classification system should

normally be:

• mutually exclusive,

• exhaustive, and

• hierarchical.

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•Mutually exclusive means that there is no taxonomic overlap of any classes (i.e., deciduous forest and evergreen forest are distinct classes).

•Exhaustive means that all land-cover classes present in the landscape are accounted for and none have been omitted.

* Hierarchical means that sublevel classes (e.g., single-family residential, multiple-family residential) may be hierarchically combined into a higher- level category (e.g., residential) that makes sense.

•Mutually exclusive means that there is no taxonomic overlap of any classes (i.e., deciduous forest and evergreen forest are distinct classes).

•Exhaustive means that all land-cover classes present in the landscape are accounted for and none have been omitted.

* Hierarchical means that sublevel classes (e.g., single-family residential, multiple-family residential) may be hierarchically combined into a higher- level category (e.g., residential) that makes sense.

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It is also important for the analyst to realize that there It is also important for the analyst to realize that there is a fundamental difference between is a fundamental difference between informationinformation classes and classes and spectralspectral classes. classes.

* Information classes* Information classes are those that human beings are those that human beings define. define.

* Spectral classes* Spectral classes are those that are inherent in the are those that are inherent in the remote sensor data and must be identified and then remote sensor data and must be identified and then labeled by the analyst. labeled by the analyst.

It is also important for the analyst to realize that there It is also important for the analyst to realize that there is a fundamental difference between is a fundamental difference between informationinformation classes and classes and spectralspectral classes. classes.

* Information classes* Information classes are those that human beings are those that human beings define. define.

* Spectral classes* Spectral classes are those that are inherent in the are those that are inherent in the remote sensor data and must be identified and then remote sensor data and must be identified and then labeled by the analyst. labeled by the analyst.

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U.S. Geological Survey Land-Use/Land-Cover Classification System for Use with Remote Sensor Data

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Feature Space Scatter plots

• Compares two image bands in feature space

• Basically two histograms displayed on two perpendicular axes.

The brighter a particular point is in The brighter a particular point is in the display, the more pixels within the display, the more pixels within the scene having that unique the scene having that unique combination of band values. combination of band values.

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Some Feature Space Concepts

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Pixel Position (X,Y)B

Band X

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64, 191

64, 64

191, 127

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Band X

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Scatterplot: High CorrelationImage with 5 pixels

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Highly Correlated

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Band X

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Low CorrelationImage with 5 pixels

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Low Correlation

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Band X

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Well Defined ClustersImage with 30 pixels and 5 clusters

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Band X

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Not So Well Defined ClustersImage with 30 pixels and 5 clusters

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Band X

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Poorly Defined Clusters: Some Class Confusion

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Band X

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Very Poorly Defined Clusters: Total Class Confusion

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Calculating Cluster MeanB

7.52.5

Mean for Red-Dots Cluster

y position Yfor Mean Cluster

x position Xfor Mean Cluster

X Mean: (2.5+2.5+5+ 5+10) / 5 = 5

Y Mean: (2.5+ 2.5+ 5+10+10) / 5 = 6

Cluster Mean = 5,6

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Calculating Cluster VarianceB

7.52.5

7.5Cluster Mean For x = 5

Cluster Mean For y = 6

Var(y)

Var(x)

For Var(x) = [(2.5 - 5)2 + (2.5 - 5)2 + (5 - 5)2 + (5 - 5)2 + (10 - 5)2 ]/5 = (6.25 + 6.25 + 0 + 0 + 25)/5 = 37.5 / 5 = 7.5

For Var(y) = [(2.5 - 6)2 + (2.5 - 6)2 + (5 - 6)2 + (10 - 6)2 + (10 - 6)2] /5 =(3.5 + 3.5 + 1 + 16 + 16/5 = 40/5 = 8

Variance(X) for Cluster = 7.5

Variance(Y) for Cluster = 8

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Variance(X) for Cluster = 7.5

Variance(Y) for Cluster = 8

Calculating Cluster Standard DeviationB

7.52.5

Direction Yin Deviation Standard

Direction Xin Deviation Standard

828.28 Deviation Standard 1

739.27.5 Deviation Standard 1

1 Standard Deviation

Band X

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Band X

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Calculating Distance in 2d Space

22 )()( Distance runrise

139.90812556252500

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Band X

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Parallelepiped View: Standard Deviation

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Band X

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Class Ellipse View: Standard Deviation

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Parametric vs. Non-Parametric Signatures

• A parametric signature is based on a statistical analysis of the pixels that are in the training site or a cluster.

– A parametric decision rule uses statistical analysis to assign pixels in an image to a particular class. When a given pixel meets the parameters of the decision rule for a given class the pixel is assigned to that class.

• A non-parametric signature is not based on statistics, but on discrete objects within feature space.

– With a non-parametric decision rule, if a pixel is located within the boundary of the non-parametric signature in feature space then that pixel will be assigned to the category represented by the signature.

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TM 30 255

Parametric: Statistically Defined

Non-Parametric: User Defined

Parametric vs. Non-Parametric Signatures

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Band X

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User Defined Signature (Non-Parametric)

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Supervised Classification• Supervised: A method in which the analyst spatially defines training sites representative of each desired class (category). The analyst then "trains" the computer software to recognize varying spectral values in two or more spectral bands associated with those training sites. This is called signature definition. After signatures for each category have been defined, the computer then uses those signatures to classify the remaining pixels in the study area.

Raw Imagery Extracted InformationTraining SitesTraining Data

CollectionSignature Creation

Data Information

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1. Define Classification Scheme

-Example: Land Use/Cover Classification Scheme, Atlanta Georgia

Metropolitan Area. Classification of Landsat TM images.

Land use/cover classes

Description

1 High-density urban

Central business districts, multi-family dwellings, commercial and industrial facilities, high impervious surface areas of institutional facilities, large transportation facilities, e.g. airports, multilane interstate/state highways

2 Low-density urban

Single family residential areas, urban recreational areas, cemeteries, playing fields, campus-like institutions, parks, schools, local roads

3 Bare land Areas with sparse vegetation (less than 20%), forest clear-cut, fallowed cropland, quarries, strip mines, rock outcrop, sand beach along rivers and lakes

4 Cropland or grassland

Row crop agriculture, orchids, vineyards, horticultural businesses, pastures, non-tilted grasses, golf courses

5 Forest Evergreen forest, deciduous forest, and mixed forest

6 Water Rivers, streams, lakes, and reservoirs

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2. Collect Training Data.

•GPS Field Data

•Refer to air photos

•Visually selecting training sites on the original Landsat TM images using human intelligence

Cotton Field

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3. Create Training Areas

1. Create training areas for each category.

2. In ERDAS Imagine, we do this by define Area of Interest (AOI)

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4. Create Signatures.

A set of statistics that defines the multi-spectral characteristics of a target phenomenon or training site.

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5. Choose Best Supervised Algorithm.

1. Minimum Distance

2. Parallelepiped

3. Maximum Likelihood

• with Null class

• without Null class

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Minimum Distance Classifier

• Every pixel is assigned to the a category based on its distance from cluster means.

• Standard Deviation is not taken into account.

• Disadvantages: generally produces poorer classification results than maximum likelihood classifiers.

• Advantages: Useful when a quick examination of a classification result is required.

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Parallelepiped

• Pixels inside of the rectangle (defined in standard deviations), are assigned the value of that class signature.

• Pixels outside of the rectangle (defined by standard deviations) are assigned a value of zero (NULL).

•Disadvantages: poor accuracy, and potential for a large number of pixels classified as NULL.

• Advantages: A speedy algorithm useful for quick results.

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Maximum Likelihood With Null Class

• Pixels inside of a stated threshold (Standard Deviation Ellipsoid) are assigned the value of that class signature.

• Pixels outside of a stated threshold (Standard Deviation Ellipsoid) are assigned a value of zero (NULL).

•Disadvantages: Much slower than the minimum distance or parallelepiped classification algorithms. The potential for a large number of NULL.

• Advantages: more “accurate” results (depending on the quality of ground truth, and whether or not the class has a normal distribution).

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Maximum Likelihood Without Null Class

• Pixels inside of a stated threshold (Standard Deviation Ellipsoid) are assigned the value of that class signature.

• Pixels outside of stated threshold (Standard Deviation Ellipsoid) are classified by minimum distance rules.

• Disadvantages: Slow Algorithm

• Advantages: High accuracy with no tied or null pixels.

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• Unsupervised: A method in which the computer separates the pixels in an image into classes (clusters) with no direction from the analyst. After the computer has completed the classifying operation the analyst determines the land-cover type for each class based on image interpretation, ground-truth information, maps, field reports.etc. , and assigns each class to a specified category (aggregation).

Unsupervised Classification

Raw Imagery (6 bands) 256 Grey Level Values

Extracted Information 11 Categories

Classified Image 80 Classes (Clusters)

Classification AggregationData Information

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Steps in Unsupervised Classification

1. Define Classification Scheme

2. Configure and Run Classifier

3. Aggregate Classification

4. Label Classes

5. Check Accuracy

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Unsupervised Classification Unsupervised Classification

• Unsupervised classification (commonly referred to as clustering) is an effective method of partitioning remote sensor image data in multispectral feature space and extracting land-cover information.

• Compared to supervised classification, unsupervised classification normally requires only a minimal amount of initial input from the analyst. This is because clustering does not normally require training data.

• Unsupervised classification (commonly referred to as clustering) is an effective method of partitioning remote sensor image data in multispectral feature space and extracting land-cover information.

• Compared to supervised classification, unsupervised classification normally requires only a minimal amount of initial input from the analyst. This is because clustering does not normally require training data.

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Unsupervised Unsupervised Classification Classification Unsupervised Unsupervised Classification Classification

• Hundreds of clustering algorithms have been Hundreds of clustering algorithms have been developed. developed.

• ERDAS Imagine uses clustering algorithm ISODATA ERDAS Imagine uses clustering algorithm ISODATA ((Iterative Self-Organizing Data Analysis)Iterative Self-Organizing Data Analysis)

• Hundreds of clustering algorithms have been Hundreds of clustering algorithms have been developed. developed.

• ERDAS Imagine uses clustering algorithm ISODATA ERDAS Imagine uses clustering algorithm ISODATA ((Iterative Self-Organizing Data Analysis)Iterative Self-Organizing Data Analysis)

Classification Based on

ISODATA Clustering

Classification Based on

ISODATA Clustering

Image Classification

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