remote sensing unsupervised image classification

Remote SensingRemote Sensing

Unsupervised Image ClassificationUnsupervised Image Classification

1. Unsupervised Image 1. Unsupervised Image ClassificationClassification

► The process requires a minimal amount of The process requires a minimal amount of initial inputinitial input from the analyst from the analyst

► A numeric operation searches for A numeric operation searches for natural natural groupinggrouping of the spectral properties of pixels of the spectral properties of pixels

► The analyst determines the information The analyst determines the information class for each spectral class class for each spectral class afterafter the the spectral classes are formedspectral classes are formed

1. Unsupervised Classification1. Unsupervised Classification

► Chain methodChain method► ISODATAISODATA► Spectral mixture analysisSpectral mixture analysis► Object-based image analysis Object-based image analysis

2. Chain Method2. Chain Method

► Pass 1 builds clusters and calculates their Pass 1 builds clusters and calculates their mean vectorsmean vectors

► Pass 2 assigns pixels to clusters based on Pass 2 assigns pixels to clusters based on the minimum-distance rulethe minimum-distance rule

Pass 1. Cluster BuildingPass 1. Cluster Building

► R, a spectral radius used to determine R, a spectral radius used to determine whether a new cluster should be formed whether a new cluster should be formed (e.g., 15 brightness)(e.g., 15 brightness)

► N, the number of pixels to be evaluated N, the number of pixels to be evaluated

between each major merging of the clusters between each major merging of the clusters (e.g., 2000)(e.g., 2000)

Pass 1. Cluster BuildingPass 1. Cluster Building

► C, a spectral distance used to determine C, a spectral distance used to determine merging clusters when N is reached (e.g., 30 merging clusters when N is reached (e.g., 30 brightness) brightness)

► Cmax, the maximum number of spectral Cmax, the maximum number of spectral clusters (categories)clusters (categories) (e.g., 20) to be (e.g., 20) to be identified identified

Pass 1Pass 1

► The operation evaluates pixels sequentially, The operation evaluates pixels sequentially, combining successive pixels into a cluster if combining successive pixels into a cluster if their spectral distance < R their spectral distance < R

► A cluster is complete when N is reached A cluster is complete when N is reached

► If the spectral distance between two If the spectral distance between two clusters is < C, the two clusters are merged, clusters is < C, the two clusters are merged, until no clusters with distance < C until no clusters with distance < C

► The new mean is the weighted average of The new mean is the weighted average of the two original clustersthe two original clusters

Pass 2. Assigning PixelsPass 2. Assigning Pixels ► Assigns pixels based on the minimum Assigns pixels based on the minimum

distance classifier distance classifier ► Manual modification based on knowledge of Manual modification based on knowledge of

the area, co- spectral plots, and interactive the area, co- spectral plots, and interactive display display

3. ISODATA Method3. ISODATA Method

► Iterative Self Organizing Data Analysis Iterative Self Organizing Data Analysis Technique Technique

ISODATAISODATA

Parameters required:Parameters required:► Cmax, the maximum number of spectral Cmax, the maximum number of spectral

clustersclusters

► T, maximum % of pixels whose classes are T, maximum % of pixels whose classes are allowed to be unchanged between iterationsallowed to be unchanged between iterations

► M, the max number of times of classifying M, the max number of times of classifying pixels and calculating cluster mean vectorspixels and calculating cluster mean vectors

ISODATAISODATA

► Minimum members in a cluster (%). For Minimum members in a cluster (%). For example, if the % <0.01, the cluster is example, if the % <0.01, the cluster is deleteddeleted

► Maximum Std Dev, when a std dev > Maximum Std Dev, when a std dev > specified Max-std-dev and the number of specified Max-std-dev and the number of members > 2*Min members, the cluster is members > 2*Min members, the cluster is split split

ISODATAISODATA

► Split separation: when the value is changed Split separation: when the value is changed from 0.0, it replaces Std Dev to determine from 0.0, it replaces Std Dev to determine the locations of the new mean vectors plus the locations of the new mean vectors plus and minus this split separation valueand minus this split separation value

► Minimum distance between cluster means. Minimum distance between cluster means. Clusters with a weighted distance < this Clusters with a weighted distance < this value (e.g., 3.0) are mergedvalue (e.g., 3.0) are merged

ISODATAISODATA

► It uses a large number of passesIt uses a large number of passes► The initial means are determined based on The initial means are determined based on

the mean and std dev of each bandthe mean and std dev of each band

►http://www.youtube.com/watch?v=ikArEGp-dv0

IterationsIterations

► Assigns each pixel using the minimum Assigns each pixel using the minimum distance classifier distance classifier

► The second to MThe second to Mthth iteration iteration re-calculate the mean vectors re-calculate the mean vectors examine Min members(%) examine Min members(%)

Max std devMax std dev

split separationsplit separation

Min distance between clustersMin distance between clusters► The iteration stops when T or M is reached The iteration stops when T or M is reached

ReadingsReadings

► Jensen 1996. 2Jensen 1996. 2ndnd Edition or 2005 3 Edition or 2005 3rdrd Edition, Edition, Introductory Digital Image Processing. Introductory Digital Image Processing. Prentice Hall. Prentice Hall.

4. Classification of Mixed Pixels4. Classification of Mixed Pixels

► Mixed pixels - when a sensor’s IFOV covers Mixed pixels - when a sensor’s IFOV covers more more than one land cover feature than one land cover feature

e.g. tree leaves, grass, and bare soile.g. tree leaves, grass, and bare soil

► Depends on the spatial resolution of Depends on the spatial resolution of sensors and sensors and the scale of featuresthe scale of features

► Sub-pixel classificationSub-pixel classification

- spectral mixture analysisspectral mixture analysis

Spectral Mixture AnalysisSpectral Mixture Analysis

► Mixed spectral signatures are compared to Mixed spectral signatures are compared to pure pure reference spectra reference spectra

► The pure signature is measured in the lab, The pure signature is measured in the lab, field, field, or from imagesor from images

► Assuming that the variation in an image is Assuming that the variation in an image is a mixture of a mixture of a limited number of featuresa limited number of features

► Estimates approx proportion of each pure Estimates approx proportion of each pure feature feature in a pixel in a pixel

Spectral Mixture Analysis ..Spectral Mixture Analysis ..

► Linear mixture models - assuming a linear Linear mixture models - assuming a linear mixture mixture of pure featuresof pure features

► Endmembers - the pure reference signaturesEndmembers - the pure reference signatures

► Weight - the proportion of the area occupied Weight - the proportion of the area occupied by by an endmemberan endmember

► Output - fraction image for each endmember Output - fraction image for each endmember showing the fraction occupied by an showing the fraction occupied by an

endmember in a pixelendmember in a pixel


Gap, water,

Mangrove, forest

Kemal Gokkaya 2008

Tole L., 2008. Changes in the built vs. non-built environment in a rapidly urbanizing region: A case study of the Tole L., 2008. Changes in the built vs. non-built environment in a rapidly urbanizing region: A case study of the Greater Toronto Area, Computers, Environment and Urban Systems, 32(5): 355-364.Greater Toronto Area, Computers, Environment and Urban Systems, 32(5): 355-364.


► Two basic conditionsTwo basic conditions

► I. The sum of fractions of all endmembers in I. The sum of fractions of all endmembers in a pixel must equal 1a pixel must equal 1

FFii = F = F11 + F + F22 + … + F + … + Fnn = 1 = 1

► II. The DN of a pixel is the sum of the DNs of II. The DN of a pixel is the sum of the DNs of endmembers weighted by their area endmembers weighted by their area fractionsfractions

DD = F = F1 1 DD11 + F + F2 2 DD22 + … + F + … + Fn n DDnn+E+E


► One DOne Dequation for each band, plus one equation for each band, plus one FFi i

equation for all bandsequation for all bands

► Number of endmembers = number of bands + 1Number of endmembers = number of bands + 1

One exact solution without the E termOne exact solution without the E term► Number of endmembers < number of bands +1Number of endmembers < number of bands +1

Fs and E can be estimated statisticallyFs and E can be estimated statistically► Number of endmembers > number of bands +1Number of endmembers > number of bands +1

No unique solutionNo unique solution


► Advantages/characteristicsAdvantages/characteristics

- a realistic representation of features- a realistic representation of features

- a deterministic, not a statistic, - a deterministic, not a statistic, methodmethod

- fuzzy set theory vs. fuzzy - fuzzy set theory vs. fuzzy classificationclassification

► Disadvantages Disadvantages

- does not account for multiple - does not account for multiple reflectionsreflections

5. Object-based Classification5. Object-based Classification

► Also called object-based image analysis Also called object-based image analysis (OBIA)(OBIA)

► vs. per pixel classification vs. per pixel classification

All classifiers so far consider the spectral All classifiers so far consider the spectral info of a single pixel regardless of its info of a single pixel regardless of its neighborsneighbors

Kutztown GEOEYE-1Kutztown GEOEYE-1

Sean Ahearn, Hunter college

5. Object-based Classification 5. Object-based Classification ....

► A two-step processA two-step process

I. segmentation of the image into objectsI. segmentation of the image into objects

II. Classiication of the objectsII. Classiication of the objects

Works at multiple scales and uses color, Works at multiple scales and uses color, shape, size, texture, pattern, and context shape, size, texture, pattern, and context information to group pixels into objectsinformation to group pixels into objects


► Two sets of characteristics can be used to Two sets of characteristics can be used to classify the objects classify the objects

The characteristics of the object itselfThe characteristics of the object itself

(spectral, texture, shape, etc.)(spectral, texture, shape, etc.)

The relationship between objectsThe relationship between objects

(connectivity, proximity, etc.)(connectivity, proximity, etc.)


► AdvantagesAdvantages

► Disadvantages Disadvantages

► E-cognition, Definiens and TrimbleE-cognition, Definiens and Trimble

ReadingsReadings

► Chapter 7Chapter 7

remote sensing unsupervised image classification

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