DIGITAL IMAGE ANALYSIS

Image Classification: Unsupervised Classification

RESEARCH TRENDS IN REMOTE SENSING

• Digital image classification– Quantitative analysis used to automate the identification

of features– Spectral pattern recognition

• Unsupervised classification• Supervised classification• Object-based classification

DIGITAL IMAGE ANALYSIS STEPS

• Remotely sensed images capture a great deal of data--each pixel ‘contains’ the (TOA) spectral response patterns of the object(s) that occur within its footprint.

• In order to use the data, most analyses require that the pixels be grouped into ‘classes’ representing physical phenomenon (i.e., convert the DN’s into information).

WHY CLASSIFY?

CLASSIFICATION CREATES INFORMATION THROUGH SIMPLIFICATION

A continuous image A boolean image

Water

BuildingsVegetation

A CLASSIFIED IMAGE HAS IMMEDIATE MEANING

• In visual image interpretation, a classification is produced by recognizing distinctive features, such as buildings, rivers or roads, or by delineating areas with similar characteristics such as deciduous or coniferous forest.

• Automated classification methods depend primarily on defining rules to assign pixels to a class based on their spectral reflectance values.

WHY CLASSIFY?

Basic elements Primary

Spatial arrangements of tone

Secondary

Tertiary

Quaternary

Tone Colour

Size Shape Texture

Pattern Height Shadow

Site Association

VISUAL VS AUTOMATED INTERPRETATION

Degreeof

complexity

Computer-based classification algorithms depend on the spectral characteristics of a pixel. Shape, and size, which are easily used in

visual interpretation, are incorporated only inobject-based classification algorithms.

Furthermore, as you know, patterns in an image can create problems (e.g., moiré patterns) and shadows can obscure features.

Per-pixelclassifiers

Contextualclassifiers

• Computer-based classification offers the benefit of processing large volumes of data and can also provide more consistent and repeatable results than visual interpretation (although it is not necessarily more accurate).

• All classifications (unsupervised, supervised & object-based) are based on the assumption that different land surfaces / classes have unique spectral response patterns across the spectrum.

• Classification of multispectral data is organizing pixels into groups / classes with more or less spectral similarity.

AUTOMATED CLASSIFICATION

• An efficient classification system should be complete and hierarchical. It should be possible to assign a class to every pixel, and more detailed classes should be organized as subdivisions of more general classes.

CLASSES?

Urban

Residential

Commercial

Etc.

Vegetation

Coniferous

Deciduous

Water

Lake

River/Stream

Etc.

Entire image

Individualclasses

Subclasses

hierarchy

• Some classes or land covers may not be uniquely identifiable from remote sensing imagery, but may be separable with additional information such as slope, aspect, and elevation (obtainable from a digital elevation model), which can provide site and association-like information.

• Some classes may themselves not be directly observable by remote sensing. However, they may have a strong correlation with observable physical features (e.g., soil types are commonly deduced by the type of vegetation they support).

CLASSES?

CLASSIFICATIONS

Anderson Classification System

URL

• Land use refers to how land is used by humans. In other words, it refers to the economic use to which land is put (e.g., commercial (stores, office buildings, apartments, etc.), industrial (factories, assembly plants), recreational, agricultural).

• Land cover refers to the vegetation, structures, or other features that cover the land (e.g., grass, trees, water, large buildings)

– Two land parcels may have similar land cover, but different land uses. For instance, an industrial plant that assembles electronic components may look, from the outside, very much like a commercial office building with a distribution warehouse.

LAND USE VERSUS LAND COVER

• Land use refers to how land is used by humans. In other words, it refers to the economic use to which land is put (e.g., commercial (stores, office buildings, apartments), industrial (factories, assembly plants), recreational, agricultural).

• Land cover refers to the vegetation, structures, or other features that cover the land (e.g., grass, trees, water, large buildings)

– Two land parcels that have similar land use may have different land use. A golf course and an office building are both commercial land uses. The former would have a land cover of grass, while the latter would be considered built-up.

LAND USE VERSUS LAND COVER

THE IMAGE CLASSIFICATION PROCESS

• Supervised classification: the analyst selects groups of pixels felt to belong to set physical classes (e.g., water, forest, urban)

• The software then assigns the remaining pixels to one of the set classes using a variety of methods.

• It is up to the analyst to determine if the spectral groupings associated with each physical class are appropriate (i.e., accuracy assessment).

THREE DISTINCT APPROACHES

Named classes

Training sites

Classified image

• Unsupervised classification: allow the software to group pixels based on the Digital Numbers (DNs) associated with each pixel (across n bands).

• A wide variety of approaches are available with which to identify the groups / clusters.

• It is up to the analyst to then identify the physical class (e.g., water, vegetation, sand) associated with each spectral group.

Pixels DN-based classes

Assign names to

classes

THREE DISTINCT APPROACHES

• Object-based classification: the analyst selects groups of adjacent pixels belonging to set physical features or objects (e.g., houses, roads, woodland) that represent the classes the analyst is interested in.

• The software then segments the data—typically using more than just the imagery (e.g., a DEM may also be included). That is, groups of pixels sharing similar spectral and (e.g.) elevation characteristics are identified.

• The groups or objects are then assigned to one of the previously identified classes.

Image segmented into blocks

Objects identified

Blocks assigned to

classes

THREE DISTINCT APPROACHES

• Land use

• Land cover

Which method would create LU, which LC ?

Named classes

Training sites

Classified image

Pixels DN-based classes

Assign names to

classes

The definition of land cover is fundamental, because in many existing classifications and legends it is confused with land use:

Land cover is the observed (bio)physical cover on the earth's surface.

When considering land cover in a very pure and strict sense, it should be confined to the description of vegetation and man-made features. Consequently, areas where the surface consists of bare rock or bare soil are land itself rather than land cover. Also, it is disputable whether water surfaces are real land cover. However, in practice, the scientific community usually includes these features within the term land cover. FAO

LAND USE VERSUS LAND COVER

Land use is characterized by the arrangements, activities and inputs people undertake in a certain land cover type to produce, change or maintain it. Definition of land use in this way establishes a direct link between land cover and the actions of people in their environment.

The following examples are a further illustration of the above definitions:

• "grassland" is a cover term, while "rangeland" or "tennis court" refer to the use of a grass cover; and

• "recreation area" is a land use term that may be applicable to different land cover types: for instance sandy surfaces, like a beach; a built-up area like a pleasure park; woodlands; etc. FAO

LAND USE VERSUS LAND COVER

The histograms are from a photograph taken with a camera that has been modified to record the green,

red and infrared wavelengths

SPECTRAL RESPONSE PATTERNS

IR reflectance

Green reflectance

Red reflectance

A scene with low reflectance

BAND HISTOGRAMS AND SCATTERPLOTS

SPECTRAL RESPONSE PATTERNS

Class (cluster) identification depends on the number of bands being used,as well as thenumber ofclasses or clusters specified.

CLUSTERS IN SPECTRAL SPACE

A greater number of bands often (but not always) makes it easier to separate out the different spectral response patterns present in an image.

Rarely, however, are clusters so clearly separated.

SPECTRAL VS GROUP DIMENSIONS

The complication with most classification routines is knowing how many classes truly exist in the data.

Select too few, and (at best) groups composed of mixed classes will be created.

Select too many, and what should be unique groups will be split into (possibly meaningless) subgroups.

How to assignthese pixels?

ISODATA CLUSTERS

ISO DATA

I - iterativeS – self-O - organizingD - dataA - analysisT - techniqueA - (application)?

Specifying 5 clusters

Pixels are assigned to the closest cluster centroid.

[A]

The centres of the new clusters are determined.

[B]

The [A] –[B] process is repeated until no major changes occur.

.

.

ISO CLUSTER

Also known as k-means, migrating means, iterative optimization.

Coniferous trees in red (Chlorophyll)

Deciduous trees in green (no

leaves)

Grass

Water

Urban/Road

Mixed trees

Shadow

False colour image(Feb)

Once an image has been clustered, the analyst attempts to associate each cluster with a physical class.

This may not always be possible, as some physical classes may share similar spectral response patterns.

UNSUPERVISED CLASSIFICATION

A false color satellite image of the Welder Wildlife Refuge (Texas) clearly differentiates at least three of the major vegetation types shown (#’d arrows): 1- riparian woodland, 2- green herbaceous vegetation, 3- spiny aster (A). Unsupervised vegetation classification resulted in 6 vegetationclasses identified (B). Accuracy assessments based on field data showed that the classification was 79-89% accurate.

• Classification of multispectral data is organizing pixels into groups / classes with more or less spectral similarity. A comparison of an supervised versus unsupervised classification is presented on the following slides.

AUTOMATED CLASSIFICATION

Named classes

Training sites

Classified image

Pixels DN-based classes

Assign names to

classes

SUPERVISED CLASSIFICATION

• Idealized spectral response patterns.

• Clusters representing 7 physical classes created through a supervised classification.

• Note that some classes overlap, and that others almost do so.

UNSUPERVISED CLASSIFICATION

• The results of an unsupervised classification.

• 15 classes were specified (although it would appear as though only 7 exist).

• Note how some groups are split into 2 or more classes, and some classes contain pixels from more than one supervised (assumed to be ‘physical’) class.

UNSUPERVISED CLASSIFICATION

• The 15 classes have been assigned to specific physical classes.

• Although the figure would appear to indicate that the 7 group classification is the ‘correct’ one, that is only because it was conducted first.

A real-world example of vegetation classes identified on a SPOT image from Cameroon.

• A hard classifier creates a booleanclassification scheme—a pixel is either in the class (e.g., forest) or not.

• A soft classifier creates an ‘ambiguous’ or continuous multilayer classification scheme—a pixel can be assigned to several classes with a likelihood associated with each class. This is equivalent to assigning sub-pixel classes to a pixel.

HARD VS SOFT CLASSIFIERS

Forest

RockSoil

Forest

RockSoil

SOFT CLASSIFIERS

Pixels can belong to more than one class.

Idrisi: Image Processing Tools: Soft Classifiers / Mixture Analysis: Unmix

ArcMap’s Class Probabilities Output

Grass

Forest

Parking lot Building roof

Dry Grass

Maximum Likelihood Classification

Fuzzy classification is another soft classification approach.

SOFT CLASSIFIERS

• PIPED (Parallelepiped Supervised Classifier)• Mindist (Minimum Distance to Means Supervised Classifier)• Maxlike (Maximum Likelihood Supervised Classifier)• KNN (K Nearest Neighbour Supervised Classifier) • Segclass (Majority-rule Segment Classifier [Supervised & Segmented])• Cluster (Unsupervised Histogram Peak Classifier)• Isodata (Iterative Self-organizing Unsupervised Classifier)• Kmeans (K-means Unsupervised Clustering Classifier) (Grouping Analysis)• SVMC (Support Vector Machine Classifier; Supervised)• RTC (Random Trees Classifier; Supervised)

These all produce a single (Boolean) output image with pixels either assigned to a known class, to an unknown class (unassigned), or to one of several computationally-determined (e.g., unsupervised) classes.

SOME OF THE HARD CLASSIFIERS AVAILABLE

• Bayclass (Bayesian Soft Supervised Classifier [probabilities])• Belclass (Dempster-Shafer Soft Supervised Classifier [ambiguity])• Mahalclass (Mahalanobis Distance Soft Classifier)• Fuzclass (Fuzzy Set Supervised Classifier) • Unmix (Linear Spectral Unmixing Supervised Classifier)• KNN and SOM can also produce soft classifications• These all produce multiple images, each associated with a specific class.

Each pixel will have a degree of membership associated with that class (either a probability, a likelihood, or a degree of membership: 0-1).

• Soft images can be ‘hardened’ by collapsing all of the class images into one, based on the most likely class for each cell (Harden)

SOME OF THE SOFT CLASSIFIERS AVAILABLE

• Unsupervised classification can help the analyst determine how many clearly identifiable spectral groups are present in an image.

• The difficulty in assigning meaningful physical classes to the spectral classes is problematic, but does reflect the overlapping nature of spectral response patterns.

• Typically both approaches (unsupervised and supervised) are taken, since they offer different perspectives on the relation between spectral response patterns and meaningful physical classes.

CONCLUSION

• Classification methods can be:– Unsupervised or supervised– Pixel-based or object-based (segmented groups

of pixels)– Soft (multiple images, one for each class) or hard

(one Boolean image)

CONCLUSION