Image ClassificationImage Classification

AssignmentAssignment

► الجيولوجيا في بعد عن االستشعار الجيولوجيا تطبيقات في بعد عن االستشعار تطبيقات

Land classificationLand classification

► Aims at label each pixel in a Aims at label each pixel in a scene to a specific land cover scene to a specific land cover typestypes

►Pixels can then be either correctly Pixels can then be either correctly classified, incorrectly classified classified, incorrectly classified or unclassifiedor unclassified

►Two main type of classificationTwo main type of classification►UnsupervisedUnsupervised►SupervisedSupervised

Unsupervised Unsupervised classificationclassification

►No previous knowledge assumed No previous knowledge assumed about the dataabout the data

►Tries to spectrally separate Tries to spectrally separate the pixelsthe pixels

►User has controls overUser has controls over►No of classesNo of classes►No of iterationsNo of iterations

Supervised Image Supervised Image ClassificationClassification

► An image classification procedure An image classification procedure that requires interaction with the that requires interaction with the analyst analyst

1. General Procedures1. General Procedures ► Training stage Training stage  - The analyst identifies the  - The analyst identifies the representative training areas (training representative training areas (training set) and develops summary statistics set) and develops summary statistics for each categoryfor each category

► Classification stage Classification stage  - Each pixel is categorized into a  - Each pixel is categorized into a land cover class land cover class

►   Output stage Output stage  - The classified image is presented in  - The classified image is presented in GIS or other forms GIS or other forms

Supervised Supervised classificationclassification

Supervised Supervised classificationclassification

http://aria.arizona.edu/slg/Vandriel.ppt

TrainingTraining

ClassifiersClassifiers

► Minimum distance Minimum distance classifierclassifier

► Parallelepiped classifier Parallelepiped classifier

1. Minimum Distance 1. Minimum Distance ClassifierClassifier

► Calculates mean of the spectral values Calculates mean of the spectral values for the training set in each band and for for the training set in each band and for each category each category

►   Measures the distance from a pixel of Measures the distance from a pixel of unknown identify to the mean of each unknown identify to the mean of each category category

►   Assigns the pixel to the category with Assigns the pixel to the category with the shortest distance the shortest distance

►   Assigns a pixel as "unknown" if the Assigns a pixel as "unknown" if the pixel is beyond the distances defined by pixel is beyond the distances defined by the analyst the analyst

2. Minimum Distance 2. Minimum Distance ClassifierClassifier(40,60)

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1. Minimum Distance 1. Minimum Distance ClassifierClassifier

Minimum Distance Classifier Minimum Distance Classifier

► Advantage Advantage  computationally simple and fast  computationally simple and fast

►   Disadvantage Disadvantage  insensitive to differences in  insensitive to differences in variance among categories variance among categories

2. Parallelepiped 2. Parallelepiped ClassifierClassifier

► Forms a decision region by the Forms a decision region by the maximum and minimum values of the maximum and minimum values of the training set in each band and for training set in each band and for each category (class)each category (class)

►   Assigns a pixel to the category Assigns a pixel to the category where the pixel falls in where the pixel falls in

►   Assigns a pixel as "unknown" if it Assigns a pixel as "unknown" if it falls outside of all regions falls outside of all regions    

2. Parallelepiped 2. Parallelepiped ClassifierClassifier

Parallelepiped Classifier Parallelepiped Classifier

► Advantage Advantage  computationally simple and fast  computationally simple and fast

    takes differences in variance into takes differences in variance into account account

► Disadvantage Disadvantage  performs poorly when the regions  performs poorly when the regions overlap because of high correlation overlap because of high correlation between categories (high covariance) between categories (high covariance)    

Parallelepiped Parallelepiped ClassifierClassifier

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Parallelepiped Parallelepiped ClassifierClassifier