2. Image Classification

3. Digital Image Processing Techniques Image Restoration Image Enhancement Image Classification Image Classification

4. Image Classification: the art and science of using the computer to interpret the image. Why do it?

5. Especially when automated computer methods oppose a long “proven” history of visual/manual image interpretation

6. However, with image classification you can make cool looking maps with more spatial detail than humans would ever draw!

7. Coop Project with Cal Fish and Game 15-meter Landsat7 Pan Sharpened Imagery Modified CWHR Classification

8. Represent detailed conditions on the ground

11. Forest Cover Classification in Cameroon

12. Semi-automated change detection A Combination of supervised image classification, polygon formation and visual editing of resulting polygons proves useful for forest monitoring.

13. Semi-Automated Change Detection Based upon 5km by 5km Blocks of Satellite Imagery

14. Image to Image Registration Accomplished with SPEAR tools In ENVI

15. The multi-date stacked image allows creation of two-date color composites that allow the visual identification of change

16. ENVI EX used to classify the image block into four classes: Forest (unchanged) Non-Forest (unchanged) Deforestation (forest changed to non-forest) Reforestation (non-forest changed to forest)

17. Training areas defined for all spectral classes visible in the image

18. Smooth the image before creating polygons

21. Area Summary Table

24. Objectives: Understand the principle of supervised classification including definition of classes and selection of training areas Understand the principle of supervised classification including definition of classes and selection of training areas Describe the maximum likelihood classification algorithm, the one most often used. Describe the maximum likelihood classification algorithm, the one most often used.

25. Image Classification Supervised Supervised  Training stage - analyst determines source identity  Classification stage  Output stage

26. Supervised Classification Select Training Areas Edit/ Evaluate Signatures Evaluate Classification Classify Image Subjective human influence selects “representative samples” of all land cover types required for the analysis < 5% of the pixels used for training. Subjective human judgment resolves problems: spectral signatures not separable, or spectral signatures redundant. Unbiased machine determines the class into which the unknown pixels are assigned (>95 % of the pixels are unknown before classification). Again subjective humans evaluate results and define new classes to change things as they desire.

27. Spectral response measurements (spectral signatures) recorded across 7 Landsat TM bands: 1, blue; 2, green; 3, red; 4 & 5, VNIR; 6, TIR and 7, SWIR Spectral response measurements (spectral signatures) recorded across 7 Landsat TM bands: 1, blue; 2, green; 3, red; 4 & 5, VNIR; 6, TIR and 7, SWIR Classification Based on Spectral Signatures 1 2 3 4 5 6 7 WaterSandForestUrbanCornHay Adapted from Lillesand and Kiefer, 1999

28. Supervised Classification C C C C C C C C CC C U UU U UU U F U F FFF FFFFF F F F F F SSS S S S S S S FF F W W W WW WWW WW W SS S S S F F F Water Sand Forest Urban Corn Hay Classification Stage compare unknown pixels to known spectral “signatures” Output Stage typically, a color-coded map Training Stage create classes 6 3 2 5 4 2 2 3 23 2 2 53 1 45 3 3 2312 21524 6 3 4 5 5 523 6 5 3 4 2 2 32 4 1 5 3 44 323 33 6 25 4 6 5 3 1 2 Adapted from Lillesand and Kiefer, 1999 identify training areas of uniform class land cover assign to most similar

29. Traditional way to view spectral signatures. BAND 3 BAND 4 red visible very near IR water forest hay corn urban sand

30. U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U S S S S S S S S C C C C C C C C C C C C H H H H H H H H H H H H H H H H H H H H H H H H H H H F F F F F F F F F F F F F F F F F F F F F F F F F W W W W W W W W W W W W W Band 3 Digital Number  Band 4 Digital Number  Supervised Classification Stage Two-band scatter diagram showing spectral separability of different land covers Two-band scatter diagram showing spectral separability of different land covers water urban hay sand corn forest 1 2 3 Determine land cover class of each pixel in the scene Adapted from Lillesand and Kiefer

31. U U U U U U U U U U U U U U U U U U U U U U U U U U U U U U S S S S S S S S C C C C C C C C C C C C H H H H H H H H H H H H H H H H H H H H H H H H H H H F F F F F F F F F F F F F F F F F F F F F F F F F W W W W W W W W W W W W W Band 3 Digital Number  Band 4 Digital Number  Supervised Classification - Maximum Likelihood Classifier Gaussian probability function computed for each pixel for each class Gaussian probability function computed for each pixel for each class 1 2 3 Adapted from Lillesand and Kiefer Pixel assigned to class for which its probability of membership is the greatest. Can be limited to some number of standard deviations or probability threshold.

32. Classification - Error Matrix Pixels as classified by ground truth Pixels as classified by the computer Classification accuracy from user’s view if computer classified a pixel as urban, how accurate was that classification? 629/689=91%…9% Error of Commission Classification accuracy from producer’s view… how many of the known urban pixels were classified by the computer as urban? 629/702=90%…10% Error of Omission Correctly classified pixels Overall Accuracy = 1033+629+385 +319+20/2578 = 93%