4. REMOTE SENSING DEFINITION

10. A) Illumination Source B) Radiation C) Interaction with the object D) Radiation sensing E) Transmission, reception and data processing F) Analysis and interpretation G) Application Process

11. SENSOR RESOLUTION

13. Spatial Resolution

14. Spectral Resolution

15. Temporal Resolution Time July 1 July 12 July 23 August 3 11 days 16 days July 2 July 8 August 3

16. Radiometric Resolution 6-bits Range 0 63 8-bits Range 0 255 0 10-bits Range 1023

17. INTRODUCTION TO IMAGE CLASSIFICATION

24. Basic Strategy: How to do it? But in reality, that is not the case. Looking at several pixels with vegetation, you’d see variety in spectral signatures. The same would happen for other types of pixels, as well.

26. Think of a pixel’s brightness in a 2-Dimensional space. The pixel occupies a point in that space. The vegetation pixel and the soil pixel occupy different points in a 2-D space.

27. With variability, the vegetation pixels now occupy a region, not a point, of n-Dimensional space. Soil pixels occupy a different region of n-Dimensional space.

30. Supervised Classification The computer then creates... Supervised classification requires the analyst to select training areas where he knows what is on the ground and then digitize a polygon within that area… Mean Spectral Signatures Known Conifer Area Known Water Area Known Deciduous Area Digital Image Conifer Deciduous Water

31. Supervised Classification Multispectral Image Information (Classified Image) Mean Spectral Signatures Spectral Signature of Next Pixel to be Classified Conifer Deciduous Water Unknown

32. The Result: Image Signatures Water Conifer Deciduous Legend: Land Cover Map

34. Unsupervised Classification Digital Image The analyst requests the computer to examine the image and extract a number of spectrally distinct clusters… Spectrally Distinct Clusters Cluster 3 Cluster 5 Cluster 1 Cluster 6 Cluster 2 Cluster 4

35. Unsupervised Classification Output Classified Image Saved Clusters Cluster 3 Cluster 5 Cluster 1 Cluster 6 Cluster 2 Cluster 4 Unknown Next Pixel to be Classified

36. Unsupervised Classification It is a simple process to regroup (recode) the clusters into meaningful information classes (the legend). The result is essentially the same as that of the supervised classification: Conif. Hardw. Water Land Cover Map Legend Water Water Conifer Conifer Hardwood Hardwood Labels

37. MODEL FORMALISM

40. Blue Green Red Near-IR Mid-IR Mean Signature 1 Candidate Pixel Mean Signature 2 It appears that the candidate pixel is closest to Signature 1. However, when we consider the variance around the signatures… Relative Reflectance Weighted Pixel Statistics Method

41. Blue Green Red Near-IR Mid-IR Mean Signature 1 Candidate Pixel Mean Signature 2 The candidate pixel clearly belongs to the signature 2 group. Relative Reflectance Weighted Pixel Statistics Method

42. Weighted Pixel Statistics Method

43. Weighted Pixel Statistics Method

44. VERIFICATION PROTOCOLS

46. Results: 1 st Synthesized Scene Synthesized Scene WOS Classification WPS Classification

47. Quantitative Comparison 1 st Synthesized Scene

48. Results: 2 nd Synthesized Scene Synthesized Scene WOS Classification WPS Classification

49. Qualitative Comparison 2 nd Synthesized Scene Synthesized Scene WOS Classification WPS Classification

50. Quantitative Comparison 2 nd Synthesized Scene

51. Results: 3 rd Synthesized Scene Synthesized Scene WOS Classification WPS Classification

52. Qualitative Comparison 3 rd Synthesized Scene Synthesized Scene WOS Classification WPS Classification

53. Quantitative Comparison 3 rd Synthesized Scene

55. SIMULATION EXPERIMENTS

57. Archaeological Site "Guachimontones", Jalisco Mexico

58. Simulation Results Scene from "Guachimontones" Original Scene WPS Classification

60. Simulation Results Scene from "La Vega" dam, Jalisco Mexico Original Scene WPS Classification

61. CONCLUDING REMARKS