1. Image Classification R.A.ALAGU RAJA Remote Sensing & GIS Lab Department of ECE Thiagarajar College of Engineering Madurai

2. Image Classification Aim is to automatically categorize all pixels in an image into land cover classes or themes. Types Supervised Classification Unsupervised Classification

3. Scanner Measurement

4. Supervised Classification The image analyst “supervises” the pixel categorization process by specifying numerical descriptors of the various land cover types present in a scene – i.e. Requirement of Training areas Steps: Training Stage Classification Stage Output Stage

5. Supervised Classification - Steps Training Stage : The image analyst identifies representative training areas and develops a numerical description of various land cover types. Classification Stage : Each pixel in the image data set is categorized into the landcover class it most closely resembles. Output Stage : The output of classification will be in three typical forms. Thematic Maps Tables Digital Data files amenable to inclusion in a GIS.

6. Supervised Classification - Steps

7. Training Stage

8. Training Set Selection

9. Scatter Diagram

10. Minimum-Distance-to-Means One of the simpler classification strategy. Mean Vector Formation. A pixel of unknown identity may be classified by computing the distance between the value of the unknown pixel and each of the category means. If the pixel is farther than an analyst – defined distance from any category mean – unknown.

11. Minimum-Distance-to-Means

12. Minimum Distance to Mean Classification

13. Parallelepiped Classification Sensitivity to category variance is introduced The range is defined by the highest and lowest DN values – Rectangular Area. Parallelepipeds – The multidimensional analogs of the rectangular areas. Very fast and computationally efficient. When category ranges overlap – Difficulties are encountered. Unknown pixels – Classified as not sure (or) arbitrarily placed in any one (or both) of the two overlapping classes.

14. Parallelepiped Classification

15. Stepped Border Parallelepiped Covariance – Tendency of spectral values to vary similarly in two bands – “Slanted Clouds of Observations”. Corn & Hay Category – Exhibits positive covariance. Water Category – Exhibits Negative Covariance. In the presence of covariance, the rectangular decision regions fit the category training data very poorly. Solution – Modifying the single rectangles into a series of rectangles with stepped borders.

16. Stepped Border Parallelepiped

17. Parallelepiped classification

18. Gaussian Maximum Likelihood Classifier The MLC quantitatively evaluates both the variance and covariance of the category spectral response patterns. The algorithm calculates the probability of an unknown pixel being a member in each category. The pixel is assigned in the most likely class (Highest probability values).

19. Probability Density Function

20. Maximum Likelihood Classifier

21. Maximum Likelihood Classification

22. Maximum Likelihood Classification Report Sl. N o. ThemesPixel 1992Area 1992 (Sq.Km) Pixel 1997Area 1997 (Sq.Km) 1.Settlement14369481888.2391566874 2058.970 2.Water15271 20.06710567 13.885 3.Hills730164 959.481722925 949.968 4.Unused Lands675868 888.132630789 828.896 5.Vegetation718914 944.697720931 947.348 6.Background22678352980.0762252914 2960.469 7.Null000 0 Total58750007720.1145875000 7720.114

23. Case Study Urban Sprawl Monitoring for Madurai City Using Multispectral Data Analysis

24. Urban Sprawl Urban sprawl is unplanned, uncontrolled spreading of urban development into areas adjoining the edge of a city. Urban sprawl leads to absence of regional planning. Urban sprawl can be resolved by Remote Sensing and Change Detection algorithms.

25. Objective To assess the urban growth by using various change detection algorithms. To recommend an optimal change detection algorithm for urban growth monitoring.

26. Study Area Madurai City, Tamilnadu Referred as Athens of Asia Second Largest City in Tamil Nadu One of the Mini Metros (20 Cities) in India – Population 14,33,251 (Acc. Census 2001) Historical City with Rich Cultural Heritage Established in 7th Century A.D. Hot Tourist Destination Latitude:9 0 50’ 59” N to 9 0 57’ 36” N Longitude:78 0 04’ 47” E to 78 0 11’ 23” E

27. Satellite Image Processing: It involves the manipulation and interpretation of satellite images with the aid of computers. Classification: To automatically categorize all pixels in an image into land cover classes or themes. Change Detection: It is process of identifying differences in the state of an object or phenomenon by observing it at different times.

28. Work Flow Image Enhancement Geometric correction Resampling to 30 meter Ground Truth Verification Image Enhancement Geometric correction Resampling to 30 meter Change Detection Algorithms Image 1 Urban Sprawl Map Image 2 Band Separator (ID, IR, CVA) Band Separator (ID, IR, CVA) Image Classifier Image Classifier Principal Component Analysis

29. Dataset Image 1 Image 2 Satellite : IRS 1B IRS P6 Sensor : LISS II LISS III Resolution : 36.25m 23.50m Date : 4 th Mar 96 19 th Mar 04 Area of coverage : 148.5 sq.km

30. Enhanced Images 1996 Enhanced Image.2004 Enhanced Image. Image 2 Image 1

31. Change Detection Algorithms Image Differencing Image Ratioing Post Classification Comparison Change Vector Analysis Principal Component Comparison

32. Image Differencing The most common technique to detect changes of an image. Each pixel from an image is subtracted from corresponding pixel in another image. I.D =t 2 – t 1 Thresholding : Chosen based on standard deviation value from the histogram plot.

33. Image Differencing Image Date 2 Difference Image = Image 1 - Image 2 Image Date 1

34. Image Differencing Threshold image (ID). Increased Decreased No change Legend Standard Deviation = 49

35. Tabulation of Result (ID) Type of ChangePixelsArea (sq. km) No Change (Black)155740140 Increased radiance (Green) 47854.3 Decreased radiance (Red) 47114.2 Change and No change area (ID). Total Area148.5

36. Comparison of two independently classified images. Compute the Error matrix. Post Classification Comparison

37. Classification Maximum Likelihood Algorithm: Creates N-dimensional ellipsoids. Probability density function is calculated for each pixel with respect to training data sets. The pixel is classified into a type which has maximum probability.

38. Classified Images 1996 Classified image.2004 Classified image. Scrub & Forest Land without Scrub Already Builtup Land Builtup Land Tank Wet Land Scrub & Forest Legend

39. PCC change map Change map (PCC). Already Builtup Land Builtup Land Land without Scrub Tank Wet Land Scrub & Forest Legend

40. Field Visit – Collection of GCPs Using GPS Receiver S. No. Name of the area Latitud e Longitude Elevatio n AccuracyFeatures 1. Kudhal Nagar tank 9.951178.104113824Vegetation 2.Sellur tank9.940778.118414827Water body 3.SITCO9.941578.149413528Urban 4.Ring road9.856578.119612722Waste land 5.Chinthamani9.887478.143813324Urban

41. Applications City Planning Mapping Population Estimation Site Selection Traffic Management and Parking studies Encroachment