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Archaeological Land Use Characterization using Multispectral Remote Sensing Data ,[object Object],[object Object],Monitoring Hidrological Variations using Multispectral SPOT-5 Data: Regional Case of Jalisco in Mexico Dr. Iván Esteban Villalón Turrubiates,  Member,   IEEE  UNIVERSIDAD DE GUADALAJARA CENTRO UNIVERSITARIO DE LOS VALLES
Overview ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Abstract ,[object Object],[object Object],[object Object]
REMOTE SENSING DEFINITION
Remote Sensing ,[object Object],[object Object],[object Object]
 
Remote Sensing ,[object Object],[object Object],[object Object]
 
Remote Sensing ,[object Object],[object Object],[object Object]
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
SENSOR RESOLUTION
Resolution ,[object Object],[object Object],[object Object],[object Object],[object Object]
Spatial Resolution
Spectral Resolution
Temporal Resolution Time July 1 July 12 July 23 August 3 11 days 16 days July 2 July 8 August 3
Radiometric Resolution 6-bits Range 0 63 8-bits Range 0 255 0 10-bits Range 1023
INTRODUCTION TO IMAGE CLASSIFICATION
Image Classification ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Typical uses ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example: Near Mary’s Peak ,[object Object],[object Object],Open Semi-open Broadleaf Mixed Young Conifer Mature Conifer Old Conifer Legend
Classification: Critical Point ,[object Object],[object Object],[object Object],[object Object],[object Object]
Basic Strategy: How to do it?  ,[object Object],[object Object]
[object Object],[object Object],[object Object],Basic Strategy: How to do it?
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.
The Classification Trick:  Deal with variability ,[object Object],[object Object]
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.
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.
Basic Strategy:  Deal with variability ,[object Object],[object Object],[object Object]
Classification Strategies ,[object Object],[object Object],[object Object],[object Object],[object Object]
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
Supervised Classification Multispectral Image Information (Classified Image) Mean  Spectral Signatures Spectral Signature of Next Pixel to be Classified Conifer Deciduous Water Unknown
The Result: Image Signatures Water Conifer Deciduous Legend: Land Cover Map
Unsupervised Classification ,[object Object],[object Object],[object Object]
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
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
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
MODEL FORMALISM
Multispectral Imaging ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Weighted Pixel Statistics Method
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
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
Weighted Pixel Statistics Method
Weighted Pixel Statistics Method
VERIFICATION PROTOCOLS
Verification Protocols ,[object Object],[object Object],[object Object],[object Object]
Results: 1 st  Synthesized Scene Synthesized Scene WOS Classification WPS Classification
Quantitative Comparison 1 st  Synthesized Scene
Results: 2 nd  Synthesized Scene Synthesized Scene WOS Classification WPS Classification
Qualitative Comparison 2 nd  Synthesized Scene Synthesized Scene WOS Classification WPS Classification
Quantitative Comparison 2 nd  Synthesized Scene
Results: 3 rd   Synthesized Scene Synthesized Scene WOS Classification WPS Classification
Qualitative Comparison 3 rd  Synthesized Scene Synthesized Scene WOS Classification WPS Classification
Quantitative Comparison 3 rd  Synthesized Scene
Remarks ,[object Object],[object Object],[object Object],[object Object]
SIMULATION EXPERIMENTS
Archaeological Land Use ,[object Object],[object Object],[object Object],[object Object],[object Object]
Archaeological Site "Guachimontones", Jalisco Mexico
Simulation Results Scene from "Guachimontones" Original Scene WPS Classification
Hidrological Variations ,[object Object],[object Object],[object Object],[object Object],[object Object]
Simulation Results Scene from "La Vega" dam, Jalisco Mexico Original Scene WPS Classification
CONCLUDING REMARKS
Remarks ,[object Object],[object Object],[object Object]
Future Work ,[object Object],[object Object],[object Object],[object Object]
[object Object],UNIVERSIDAD DE GUADALAJARA CENTRO UNIVERSITARIO DE LOS VALLES THANK YOU! Questions?

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IGARSS 2011.ppt

Editor's Notes

  1. Information usually gathered from spacecraft or an airplane, but can be a handheld or boom-mounted device. Originally defined in 1960’s according to Jensen, to encompass photogrammertry and information gathered from nonphotometric sources.