chris spry final rs project

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Remote Sensing Image Classification Chris Spry ESCI 4170 12.08.09

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Page 1: Chris Spry Final Rs Project

Remote Sensing Image Classification

Chris SpryESCI 417012.08.09

Page 2: Chris Spry Final Rs Project

Project Description

• Collected Groundtruth data to use in the creation of feature classes for classification

• Utilized 3 classification methods and determined the best method for making a land cover map of University Area

• Produced Error Matrices for each method• Produced Permeability and Natural Vegetation Maps

and determined the percentage and surface area of each land cover type

Page 3: Chris Spry Final Rs Project

Quickbird Image of the University Area of Charlotte NC (2004)

Page 4: Chris Spry Final Rs Project

•Quickbird uses Ball Aerospace’s Global Imaging System 2000•Panchromatic imagery at 60-70 cm resolution•Multispectral imagery at 2.4 and 2.8 meter

resolutions•Imaged used for this project is 2.4 meter

panchromatic•4 bands utilized in the visible red, green, blue, and

near infrared regions of the electromagnetic spectrum

Page 5: Chris Spry Final Rs Project

3 Types of Classifiers Used

• Parallelepiped– This classification method uses uses a simple decision rule to classify

multispectral data based on highest and lowest digital numbers for each band. Basically there is a box drawn which represents these numbers around a known pixel in feature space. Only pixels within this boundary are classified into the same group, unless the user specifies standard deviation or distance thresholds outside of the box.

• Minimum Distance- This classification method calculates the center for each training area or group of known class of pixels. It then calculates the the Euclidean distance from each unknown pixel to the center for the known class of pixels. All pixels are classified to the closest class unless the user specifies standard deviation or distance thresholds, in which case some pixels may be unclassified if they do not meet the selected criteria.

• Maximum Likelihood- (Best Choice) This classification method

assumes that the training area of known pixels for each class has a normal distribution. The method then determines the probability that an unknown pixel belongs to a certain class and assigns each pixel to the class that has the highest probability. All pixels are classified unless probability thresholds are established.

Page 6: Chris Spry Final Rs Project

Feature ClassesFeature Class Band Min Max St. Dev.

Conifer Band 1 17 20 0.633

Band 2 18 27 1.491

Band 3 7 13 1.043

Band 4 14 56 7.95

Deciduous Band 1 19 23 0.794

Band 2 21 29 1.341

Band 3 11 18 1.269

Band 4 16 49 5.003

Concrete/White Roof Band 1 40 58 1.941

Band 2 65 96 3.329

Band 3 44 70 2.85

Band 4 47 78 3.018

Asphalt/GreyRoof/Gravel Band 1 26 31 0.759

Band 2 36 43 1.162

Band 3 21 27 1.039

Band 4 20 30 1.299

Bare Soil Band 1 24 31 1.459

Band 2 42 56 3.127

Band 3 36 51 3.169

Band 4 42 60 3.667

Short Grass Band 1 22 26 0.681

Band 2 33 38 1.062

Band 3 22 28 1.335

Band 4 54 73 2.57

Long Grass Band 1 25 30 0.985

Band 2 37 47 1.746

Band 3 29 39 1.764

Band 4 48 61 2.406

Water 1 Band 1 20 23 0.493

(Green Sedimentation) Band 2 30 32 0.512

Band 3 16 19 0.509

Band 4 6 10 0.681

Water 2 Band 1 23 26 0.526

(Brown Sedimentation) Band 2 42 46 0.662

Band 3 32 32 0.532

Band 4 11 11 0.607

Water 3 Band 1 19 21 0.484

(Dark/Blue Sedimentation) Band 2 20 23 0.574

Band 3 8 10 0.509

Band 4 5 8 0.779

Page 7: Chris Spry Final Rs Project

Parallelepiped Classified Map

LegendConiferDeciduousConcrete/RooftopsAsphalt/Houses/GravelBare SoilLawn grassNatural GrassWaterUnclassified

Page 8: Chris Spry Final Rs Project

Error Matrix for Parallelepiped

Total Accuracy: 82.5 %

Class Conifer Deciduous Concrete/White RoofAsphalt/GreyRoof/GravelBare Soil Short Grass Long Grass Water 1 Water 2 Water 3Conifer 184 13 0 0 0 3 0 0 0 0Deciduous 8 431 0 0 0 1 0 0 0 0Concrete/White Roof 0 0 94 105 0 0 0 0 0 0Asphalt/GreyRoof/Gravel 0 0 0 301 0 0 0 0 0 0Bare Soil 0 0 0 0 111 0 0 0 0 0Short Grass 0 0 0 0 0 195 108 0 0 0Long Grass 0 6 0 0 36 9 451 0 0 0Water 1 0 0 0 0 0 0 0 0 0 0Water 2 0 0 0 0 0 0 0 0 1131 0Water 3 0 0 0 0 0 0 0 0 0 753Unclassified 0 2 29 0 0 0 0 246 239 80

Total 192 452 123 406 147 208 559 246 1370 833

Page 9: Chris Spry Final Rs Project

Errors of Omission/CommissionParallelepiped

Feature Class Omission Comission Omission Commission Prod. Acc. User Acc. User Acc. Prod. Acc.(Percent) (Percent) (Pixels) (Pixels) (Percent) (Percent) (Pixels) (Pixels)

Conifer 2.30% 2.60% 13/543 14/541 97.10% 97.40% 527/541 527/543Deciduous 2.50% 2.40% 14/542 13/548 97.20% 9.50% 52/548 527/542Concrete/White Roof 9.80% 13.70% 78/789 113/824 90.10% 86.30% 711/824 711/789Asphalt/GreyRoof/Gravel 0.00% 20.50% 0/292 78/380 100.00% 76.80% 292/380 292/292Bare Soil 0.00% 24.50% 0/129 36/147 100.00% 100.00% 129/129 129/129Short Grass 35.60% 6.30% 108/303 13/208 64.30% 93.80% 195/208 195/303Long Grass 8.96% 19.30% 45/502 108/559 89.80% 80.70% 451/559 451/502Water 1 0.00% 100.00% 0/0 246/246 0.00% 0.00% 0/246 0/0Water 2 0.00% 18.10% 0/492 109/601 100.00% 35.90% 492/1370 492/492Water 3 0.00% 11.10% 0/520 65/585 100.00% 88.90% 520/585 520/520

Page 10: Chris Spry Final Rs Project

Minimum Distance Classified Map

LegendConiferDeciduousConcrete/RooftopsAsphalt/Houses/GravelBare SoilLawn grassNatural GrassWaterUnclassified

Page 11: Chris Spry Final Rs Project

Error Matrix for Minimum Distance

Total Accuracy: 83.5 %

Class Conifer Deciduous Concrete/White RoofAsphalt/GreyRoof/GravelBare Soil Short Grass Long Grass Water 1 Water 2 Water 3Conifer 170 30 0 0 0 0 0 0 0 0Deciduous 22 422 0 0 0 0 0 18 0 0Concrete/White Roof 0 0 61 0 0 0 0 0 0 0Ashalt/GreyRoof/Gravel 0 0 0 406 0 0 0 1 0 0Bare Soil 0 0 2 0 147 0 0 0 0 0Short Grass 0 0 0 0 0 205 25 0 0 0Long Grass 0 6 0 0 0 3 519 0 0 0Water 1 0 0 0 0 0 0 0 101 0 0Water 2 0 0 0 0 0 0 0 0 1021 0Water 3 0 0 0 0 0 0 0 0 0 573Unclassified 0 0 62 0 0 0 15 145 349 260

Total 192 452 123 406 147 208 559 246 1370 833

Page 12: Chris Spry Final Rs Project

Errors of Omission/CommissionMinimum Distance

Feature Class Omission Comission Omission Commission Prod. Acc. User Acc. User Acc. Prod. Acc.(Percent) (Percent) (Pixels) (Pixels) (Percent) (Percent) (Pixels) (Pixels)

Conifer 15.00% 11.50% 30/200 22/192 85.00% 88.50% 170/192 170/200Deciduous 8.60% 7.90% 40/462 36/452 91.00% 93.36% 422/452 422/462Concrete/White Roof 0.00% 52.03% 0/61 64/123 100.00% 49.50% 61/123 61/61Asphalt/GreyRoof/Gravel 0.01% 0.00% 1/407 0/406 99.70% 100.00% 406/406 406/407Bare Soil 1.30% 0.00% 2/149 0/147 98.60% 100.00% 147/147 147/149Short Grass 10.90% 1.40% 25/230 3/208 89.10% 98.50% 205/208 205/230Long Grass 1.70% 7.10% 9/528 40/559 99.40% 86.60% 519/559 519/522Water 1 0.00% 66.70% 0/101 164/246 100.00% 41.00% 101/246 101/101Water 2 0.00% 25.50% 0/1021 349/1370 100.00% 74.50% 1021/1370 1021/1021Water 3 0.00% 31.20% 0/573 260/833 100.00% 67.90% 573/843 573/573

Page 13: Chris Spry Final Rs Project

Maximum Likelihood Classified Map

LegendConiferDeciduousConcrete/RooftopsAsphalt/Houses/GravelBare SoilLawn grassNatural GrassWaterUnclassified

Page 14: Chris Spry Final Rs Project

Error Matrix for Maximum Likelihood

Total Accuracy: 97.39 %

Class Conifer Deciduous Concrete/White RoofAsphalt/GreyRoof/GravelBare Soil Short Grass Long Grass Water 1 Water 2 Water 3Conifer 130 0 0 0 0 3 0 0 0 0Deciduous 62 446 0 0 0 1 0 18 0 0Concrete/White Roof 0 0 123 0 0 0 0 0 0 0Asphalt/GreyRoof/Gravel 0 0 0 406 0 0 0 1 0 0Bare Soil 0 0 0 0 129 0 0 0 0 0Short Grass 0 0 0 0 0 195 0 0 0 0Long Grass 0 6 0 0 18 9 559 0 0 0Water 1 0 0 0 0 0 0 0 227 0 0Water 2 0 0 0 0 0 0 0 0 1370 0Water 3 0 0 0 0 0 0 0 0 0 833Unclassified 0 0 0 0 0 0 0 0 0 0

Total 192 452 123 406 147 208 559 246 1370 833

Page 15: Chris Spry Final Rs Project

Errors of Omission/CommissionMaximum Likelihood

Feature Class Omission Commission Omission Comission User Acc. Prod. Acc. User Acc. Prod. Acc.(Percent) (Percent) (Pixels) (Pixels) (Percent) (Percent) (Pixels) (Pixels)

Conifer 2.26 32.29 3/133 62/192 67.71 97.74 130/192 130/133Deciduous 15.37 1.33 81/257 6/452 98.67 84.63 446/452 446/527Concrete/White Roof 0.00 0.00 0/123 0/123 100.00 100.00 123/123 123/123Asphalt/GreyRoof/Gravel 0.25 0.00 1/407 0/406 100.00 99.75 406/406 406/407Bare Soil 0.00 12.24 0/129 18/147 87.76 100.00 129/147 129/129Short Grass 0.00 6.25 0/195 13/208 93.75 100.00 195/208 195/195Long Grass 5.57 0.00 33/592 0/559 100.00 94.43 559/559 559/592Water 1 0.00 7.72 0/227 19/246 92.28 100.00 227/246 227/227Water 2 0.00 0.00 0/1370 0/1370 100.00 100.00 1370/1370 1370/1370Water 3 0.00 0.00 0/833 0/833 100.00 100.00 833/833 833/833

Page 16: Chris Spry Final Rs Project

Percentage of Classes in Area

Feature Class Area of Class (Km2) Percentage of Total Area

Conifer 5.851 7.32%

Deciduous 42.021 52.55%

Concrete/White Roof 2.026 2.53%

Asphalt/GreyRoof/Gravel 7.246 9.06%

Bare Soil 4.731 5.92%

Short Grass 3.215 4.02%

Long Grass 14.379 17.98%

Water 1 0.121 0.15%

Water 2 0.061 0.09%

Water 3 0.306 0.38%

Totals 79.957 100.00%

Page 17: Chris Spry Final Rs Project

Permeability of Surfaces

Legend Percentage

Permeable 88.40%Impermeable 11.60%

*Permeable Area includes the following classes: Conifer, Deciduous, Short Grass, Long Grass, Bare Soil, All Water Classes*Impermeable Area includes the following classes: Concrete/White manmade materials, Asphalt/Gray manmade materials

Page 18: Chris Spry Final Rs Project

Natural Vegetation vs. Human Influenced Areas

Legend   PercentageNatural   77.86%Human Influenced   22.15%

*Human Influenced Area includes the following classes: Asphalt/Gray Roofs, Concrete/White rooftops, Short Grass, Bare Soil, All Water Classes•Natural Area includes the following classes: Conifers, Deciduous, Long Grass