Supervised Classification Portland Metro

1. Object Based Supervised Classification
Portland Metro First Classification
Classes: Rule Applied Bands Parametric Expert Rule
Standard nearest Green, Red, NIR,
Bare Soil neighbor NDVI, DEM Mean
4-Band Image NDVI Image Grass
Others
to all classes PCA-1 Ratio DEM < 3Ft.
Hi-Reflective Roof
Paved Surfaces
Residential Roof
Tree DEM > 3Ft.
Second Classification
Classes: Rule Applied Bands Parametric Expert Rule
Blue, Green, Red, NIR,
Standard nearest NDV, PCA-2, PCA-3,
Blue Buildings neighbor 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
PCA Image DEM Image PCA-3
Logical = 'AND'
< Upper Value
Logical = 'OR'
Bands Spatial Date Pixel Depth Projection Source/Platform > Upper Value
Resolution (bit)
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
Final Classification Classify Others
Assign class: Unclassified at Level 1: Others
Blue Buildings Roads
Grass Soil
Others Tree
Pool White & Gray Buildings
Accuracy of Training Samples First Classification
Hi-Reflective Paved Residential
User Class Sample Bare Soil Grass Roof Others Surfaces Roof Tree Sum
Bare Soil 0 0 0 0 0 0 0 0
Grass 0 42 0 0 0 0 0 42
Random polygons were generated using a random number generated in ArcGIS Hi-Reflective Roof
Others
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
and added as a field to the Final classification shapefile. The 50 polygons were selected Paved Surfaces 0 0 0 0 0 0 0 0
by the random number then saved as a separate shapefile and overlayed
Residential Roof 0 0 0 0 0 2 0 2
Tree 0 0 0 0 0 0 58 58
onto the original image. The random "Reference" polygons classification was then assessed Sum 4 42 17 1 38 48 58
against the known classification class to come up with a total overall accuracy percent. Producer 0 1 0 0 0 0.041666667 1
User undefined 1 undefined undefined undefined 1 1
Rand_Num *Ref_Num Class_name Class_num Accuracy ClassNum Class
KIA Per Class 0 1 0 0 0 0.03236246 1
Overall Accuracy 0.49038462
0.000000 4 Tree 4 0 1 Buildings KIA 0.42039958
0.000008 1 Roads 2 1 2 Other Impervious
Accuracy of Training Samples Final Classification
0.000010 1 White & Gray Buildings 1 0 3 Unmanaged Soil Sum
0.000013 2 Roads 2 0 4 Trees and Shrubs Blue Buildings 80 0 0 0 0 0 0 0 80
0.000014 1 White & Gray Buildings 1 0 5 Grass Grass 0 42 0 0 0 0 0 0 42
Others 0 0 0 0 0 0 0 0 0
0.000015 2 Roads 2 0 6 Simmings Pool Pool 0 0 0 12 0 0 0 0 12
0.000017 2 Soil 3 1 7 Other Water Bodies Roads 0 0 1 0 0 0 0 0 1
0.000201 1 White & Gray Buildings 1 0
Soil 0 0 0 0 0 13 0 0 13
Tree 0 0 0 0 0 0 58 0 58
0.000201 4 Grass 5 1 *Ref_Num = observed White & Grey Blgs 0 0 0 0 0 0 0 92 92
0.000202 4 Tree 4 0 on original image file
Sum 80 42 1 12 0 13 58 92
Total accuracy points 50 in ArcGIS Producer 1 1 0 1 undefined 1 1 1
Count if 0 37 Accuracy
User 1 1 undefined 1 0 1 1 1
KIA Per Class 1 1 0 1 undefined 1 1 1
Count if 1 13 37/50 0=match Overall Accuracy 0.996644295
Overall Accuracy 74% 1=no match
KIA 0.995649572