Supervised Classifcation Portland Metro
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Object Based Supervised Classification Portland Metro 4-Band Image NDVI Image PCA Image DEM Image Blue Buildings Grass Others Pool Roads Soil Tree White & Gray Buildings Final Classification Object-based Urban Classification using Fine Resolution Imagery Portland, Oregon The classification project as proposed is part of the Carbon and Water Observatory Networks for Eco-Districts collaborative project between Portland Oregon and Phoenix Arizona. The goal of the project is to assess and measure Greenhouse gases by : 1) identifying neighborhood scale “eco-districts” for long-term study; 2) establish a sampling network for characterizing urban carbon fluxes and developing urban carbon budgets; (3) analyze existing data about water consumption and stormwater systems in select neighborhoods for developing predictions about the impacts of global change on urban water cycles; (4) engage in a series of dialogs with community partners to develop a long-term monitoring and evaluation approach to guide city-wide mitigation and adaptation efforts. 1 The first objective “identification of eco-districts” involves the classification of urban features within the Portland Metro urban boundaries. For the purpose of this term project, I will use only the “test” sites for classification; there are 5 test sites in all. The classification is done using Definiens E-Cognition– an object based image processing program. Image preparation and band generation was completed using ERDAS. Bands Spatial Resolution Date Pixel Depth (bit) Projection Source/Platform 1. Blue 2. Green 3. Red 4. NIR 5. NDVI 6. Principal Component 1 7. Principal Component 2 8. Principal Component 3 4 x 4 feet 2008 8 bit unsigned integer NAD83 HARN State Plane OR North FIPS- 3601 Metro 2008 Aerial Photo LIDAR(DEM) 3 x 3 feet 2005 Oregon LIDAR Consortium 2005 Accuracy of Training Samples First Classification User Class \ Sample Bare Soil Grass Hi-Reflective Roof Others Paved Surfaces Residential Roof Tree Sum Bare Soil 0 0 0 0 0 0 0 0 Grass 0 42 0 0 0 0 0 42 Hi-Reflective Roof 0 0 0 0 0 0 0 0 Others 0 0 0 0 0 0 0 0 Paved Surfaces 0 0 0 0 0 0 0 0 Residential Roof 0 0 0 0 0 2 0 2 Tree 0 0 0 0 0 0 58 58 Sum 4 42 17 1 38 48 58 Producer 0 1 0 0 0 0.041666667 1 User undefined 1 undefined undefined undefined 1 1 KIA Per Class 0 1 0 0 0 0.03236246 1 Overall Accuracy 0.49038462 KIA 0.42039958 Accuracy of Training Samples Final Classification User Class \ Sample Blue Bldgs Grass Others Pool Roads Soil Tree White & Grey Bldgs Sum Blue Buildings 80 0 0 0 0 0 0 0 80 Grass 0 42 0 0 0 0 0 0 42 Others 0 0 0 0 0 0 0 0 0 Pool 0 0 0 12 0 0 0 0 12 Roads 0 0 1 0 0 0 0 0 1 Soil 0 0 0 0 0 13 0 0 13 Tree 0 0 0 0 0 0 58 0 58 White & Grey Blgs 0 0 0 0 0 0 0 92 92 Sum 80 42 1 12 0 13 58 92 Producer 1 1 0 1 undefined 1 1 1 User 1 1 undefined 1 0 1 1 1 KIA Per Class 1 1 0 1 undefined 1 1 1 Overall Accuracy 0.996644295 KIA 0.995649572 First Classification Classes: Rule Applied Bands Parametric Expert Rule Bare Soil Standard nearest neighbor Green, Red, NIR, NDVI, DEM Mean Grass to all classes PCA-1 Ratio DEM < 3Ft. Others Hi-Reflective Roof Paved Surfaces Residential Roof Tree DEM > 3Ft. Second Classification Classes: Rule Applied Bands Parametric Expert Rule Blue Buildings Standard nearest neighbor Blue, Green, Red, NIR, NDV, PCA-2, PCA-3, DEM Mean DEM > 7Ft. Pool to all classes PCA-1, PCA-3, NDVI Ratio Soil Other Paved Surfaces White & Gray Buildings DEM > 7Ft. Roads Expert Rules applied Blue, NDVI Mean Blue <= Upper Value NDVI < Upper Value Logical = 'AND' PCA-3 < Upper Value Logical = 'OR' > Upper Value Classification Process Rule Set Classify Tree, Grass, Others Multiresolution Segmentation: 10 [shape:0.10 compct.:0.5] creating 'Level 1' Classification: at Level 1: Bare Soil, Grass, Hi-Reflective Roof, Others, Paved Surfaces, Residential Roof, Tree Assign Class: Unclassified at Level 1: Bare Soil, Hi-Reflective Roof, Others, Paved Surfaces, Residential Roof Classify Blue Bldgs, Soil, White&Gray Bldgs, Paved Surfaces, Pool, Other Land Cover Multiresolution Segmentation: Unclassified at Level 1: 25 [shape:0.1 compct.:0.5] Classification: Unclassified at Level 1: Blue Buildings, Others, Paved Surfaces, Pool, Soil, White & Gray Buildings Classify Roads Assign class: Unclassified at Level 1: Paved Surfaces Classification: Unclassified at Level 1: Roads Classify Others Assign class: Unclassified at Level 1: Others Random polygons were generated using a random number generated in ArcGIS and added as a field to the Final classification shapefile. The 50 polygons were selected by the random number then saved as a separate shapefile and overlayed onto the original image. The random "Reference" polygons classification was then assessed against the known classification class to come up with a total overall accuracy percent. Rand_Num *Ref_Num Class_name Class_num Accuracy ClassNum Class 0.000000 4 Tree 4 0 1 Buildings 0.000008 1 Roads 2 1 2 Other Impervious 0.000010 1 White & Gray Buildings 1 0 3 Unmanaged Soil 0.000013 2 Roads 2 0 4 Trees and Shrubs 0.000014 1 White & Gray Buildings 1 0 5 Grass 0.000015 2 Roads 2 0 6 Simmings Pool 0.000017 2 Soil 3 1 7 Other Water Bodies 0.000201 1 White & Gray Buildings 1 0 0.000201 4 Grass 5 1 0.000202 4 Tree 4 0 50 Count if 0 37 Accuracy Count if 1 13 0=match 74% 1=no match Total accuracy points *Ref_Num = observed on original image file in ArcGIS 37/50 Overall Accuracy Donnych Diaz - Geog 581 Winter 2010
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Supervised Classification Portland Metro
Transcript of Supervised Classifcation Portland Metro
Object Based Supervised ClassificationPortland Metro
4-Band Image NDVI Image
PCA Image DEM Image
Blue BuildingsGrassOthersPool
RoadsSoilTreeWhite & Gray Buildings
Final Classification
Object-based Urban Classification using Fine Resolution Imagery Portland, Oregon
The classification project as proposed is part of the Carbon and Water Observatory Networks for Eco-Districts collaborative project between Portland Oregon and Phoenix Arizona. The goal of the project is to assess and measure Greenhouse gases by : 1) identifying neighborhood scale “eco-districts” for long-term study; 2) establish a sampling network for characterizing urban carbon fluxes and developing urban carbon budgets; (3) analyze existing data about water consumption and stormwater systems in select neighborhoods for developing predictions about the impacts of global change on urban water cycles; (4) engage in a series of dialogs with community partners to develop a long-term monitoring and evaluation approach to guide city-wide mitigation and adaptation efforts.1
The first objective “identification of eco-districts” involves the classification of urban features within the Portland Metro urban boundaries. For the purpose of this term project, I will use only the “test” sites for classification; there are 5 test sites in all. The classification is done using Definiens E-Cognition– an object based image processing program. Image preparation and band generation was completed using ERDAS.
Bands Spatial Resolution
Date Pixel Depth (bit)
Projection Source/Platform 1. Blue 2. Green 3. Red 4. NIR 5. NDVI 6. Principal Component 1 7. Principal Component 2 8. Principal Component 3
4 x 4 feet
2008 8 bit unsigned integer
NAD83 HARN State Plane OR North FIPS-3601
Metro 2008 Aerial Photo
LIDAR(DEM) 3 x 3 feet 2005 Oregon LIDAR Consortium 2005
Accuracy of Training Samples First ClassificationUser Class \ Sample Bare Soil Grass
Hi-Reflective Roof Others
Paved Surfaces
Residential Roof Tree Sum
Bare Soil 0 0 0 0 0 0 0 0Grass 0 42 0 0 0 0 0 42Hi-Reflective Roof 0 0 0 0 0 0 0 0Others 0 0 0 0 0 0 0 0Paved Surfaces 0 0 0 0 0 0 0 0Residential Roof 0 0 0 0 0 2 0 2Tree 0 0 0 0 0 0 58 58Sum 4 42 17 1 38 48 58
Producer 0 1 0 0 0 0.041666667 1User undefined 1 undefined undefined undefined 1 1KIA Per Class 0 1 0 0 0 0.03236246 1Overall Accuracy 0.49038462KIA 0.42039958
Accuracy of Training Samples Final ClassificationUser Class \ Sample Blue Bldgs Grass Others Pool Roads Soil Tree White & Grey Bldgs SumBlue Buildings 80 0 0 0 0 0 0 0 80Grass 0 42 0 0 0 0 0 0 42Others 0 0 0 0 0 0 0 0 0Pool 0 0 0 12 0 0 0 0 12Roads 0 0 1 0 0 0 0 0 1Soil 0 0 0 0 0 13 0 0 13Tree 0 0 0 0 0 0 58 0 58White & Grey Blgs 0 0 0 0 0 0 0 92 92Sum 80 42 1 12 0 13 58 92
Producer 1 1 0 1 undefined 1 1 1User 1 1 undefined 1 0 1 1 1KIA Per Class 1 1 0 1 undefined 1 1 1Overall Accuracy 0.996644295KIA 0.995649572
First ClassificationClasses: Rule Applied Bands Parametric Expert Rule
Bare SoilStandard nearest
neighborGreen, Red, NIR,
NDVI, DEM Mean Grass to all classes PCA-1 Ratio DEM < 3Ft. Others Hi-Reflective Roof Paved Surfaces Residential Roof Tree DEM > 3Ft.
Second ClassificationClasses: Rule Applied Bands Parametric Expert Rule
Blue BuildingsStandard nearest
neighbor
Blue, Green, Red, NIR, NDV, PCA-2, PCA-3,
DEM Mean DEM > 7Ft. Pool to all classes PCA-1, PCA-3, NDVI Ratio Soil Other Paved Surfaces White & Gray Buildings DEM > 7Ft. Roads Expert Rules applied Blue, NDVI Mean Blue <= Upper Value
NDVI < Upper ValueLogical = 'AND'
PCA-3 < Upper ValueLogical = 'OR'> Upper Value
Classification Process Rule Set Classify Tree, Grass, Others Multiresolution Segmentation: 10 [shape:0.10 compct.:0.5] creating 'Level 1' Classification: at Level 1: Bare Soil, Grass, Hi-Reflective Roof, Others, Paved Surfaces, Residential Roof, Tree Assign Class: Unclassified at Level 1: Bare Soil, Hi-Reflective Roof, Others, Paved Surfaces, Residential Roof
Classify Blue Bldgs, Soil, White&Gray Bldgs, Paved Surfaces, Pool, Other Land Cover Multiresolution Segmentation: Unclassified at Level 1: 25 [shape:0.1 compct.:0.5] Classification: Unclassified at Level 1: Blue Buildings, Others, Paved Surfaces, Pool, Soil, White & Gray Buildings
Classify Roads Assign class: Unclassified at Level 1: Paved Surfaces Classification: Unclassified at Level 1: Roads Classify Others Assign class: Unclassified at Level 1: Others
Random polygons were generated using a random number generated in ArcGIS and added as a field to the Final classification shapefile. The 50 polygons were selectedby the random number then saved as a separate shapefile and overlayedonto the original image. The random "Reference" polygons classification was then assessedagainst the known classification class to come up with a total overall accuracy percent.Rand_Num *Ref_Num Class_name Class_num Accuracy ClassNum Class0.000000 4 Tree 4 0 1 Build ings0.000008 1 Roads 2 1 2 Other Impervious0.000010 1 White & Gray Build ings 1 0 3 Unmanaged Soil0.000013 2 Roads 2 0 4 Trees and Shrubs0.000014 1 White & Gray Build ings 1 0 5 Grass0.000015 2 Roads 2 0 6 Simmings Pool0.000017 2 Soil 3 1 7 Other Water Bodies0.000201 1 White & Gray Build ings 1 00.000201 4 Grass 5 10.000202 4 Tree 4 0
50Count if 0 37 AccuracyCount if 1 13 0=match
74% 1=no match
Tota l accuracy points*Ref_Num = observed on orig ina l image file
in ArcGIS37/50
Overall Accuracy
Donnych Diaz - Geog 581 Winter 2010
