Image Classification

Assignment تطبيقات الاستشعار عن بعد في الجيولوجيا

Land classification Aims at label each pixel in a scene to a specific land cover types Pixels can then be either correctly classified, incorrectly classified or unclassified Two main type of classification Unsupervised Supervised

Unsupervised classification No previous knowledge assumed about the data Tries to spectrally separate the pixels User has controls over No of classes No of iterations

Supervised Image Classification An image classification procedure that requires interaction with the analyst

1. General Procedures Training stage  - The analyst identifies the representative training areas (training set) and develops summary statistics for each category Classification stage  - Each pixel is categorized into a land cover class  Output stage  - The classified image is presented in GIS or other forms

Supervised classification

Supervised classification

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

Training

Parallelepiped classifier Classifiers Minimum distance classifier Parallelepiped classifier

1. Minimum Distance Classifier Calculates mean of the spectral values for the training set in each band and for each category  Measures the distance from a pixel of unknown identify to the mean of each category  Assigns the pixel to the category with the shortest distance  Assigns a pixel as "unknown" if the pixel is beyond the distances defined by the analyst

2. Minimum Distance Classifier (40,60) 0,0

1. Minimum Distance Classifier

Minimum Distance Classifier Advantage  computationally simple and fast  Disadvantage  insensitive to differences in variance among categories

2. Parallelepiped Classifier Forms a decision region by the maximum and minimum values of the training set in each band and for each category (class)  Assigns a pixel to the category where the pixel falls in  Assigns a pixel as "unknown" if it falls outside of all regions  

2. Parallelepiped Classifier

Parallelepiped Classifier Advantage  computationally simple and fast   takes differences in variance into account Disadvantage  performs poorly when the regions overlap because of high correlation between categories (high covariance)  

Parallelepiped Classifier ?

Parallelepiped Classifier