Emerge Process Document for the King County Department of Natural Resources

For Impervious Surfaces Distribution for Eastern King County

 

July 1, 2002
Table of Contents

I.     Introduction.. I-2

II.    Pre-processing.. II-2

ERDAS Imagine Procedures. II-2

Step 1:  Impervious Surface Quality Assessment II-2

Step 2:  Emerge Image Access Satellite Overpass Mosaic. II-2

Step 3:  Positional Accuracy with Ancillary Datasets Verification. II-2

III.   Impervious Strata.. III-3

Goal. III-3

ERDAS Imagine Procedures. III-3

Step 1:  NDVI Threshold. III-3

Step 2:  Potential Thresholds Effectiveness Evaluation. III-3

Step 4:  Impervious File Creation. III-3

Step 5:  Isodata Cluster. III-3

IV.   Unsupervised Classification.. IV-3

Goal. IV-3

Procedures. IV-4

Step 1:  Possible Pervious Clusters Identification. IV-4

Step 2:  Category Labels Assignment IV-4

V.    Recovery from Spectral Aberrations Caused by Gamma Correction of Emerge Imagery (Addendum Above & Beyond) V-4

Goal. V-4

ERDAS Imagine Procedures. V-4

Step 1:  Isodata Cluster. V-4

Step 2:  Cluster Identification. V-4

Step 3:  File Recode. V-4

Step 4:  File Appending. V-4

VI.   Minimum Mapping Unit (MMU) VI-5

Goal. VI-5

ERDAS Imagine Procedures. VI-5

Step 1:  File Recode. VI-5

Step 2:  Clump Elimination. VI-5

Step 3:  Focal Filter. VI-5

Step 4:  Clump. VI-5

Step 5:  Mask. VI-5

VII.  Export Product.. VII-5

Goal. VII-5

ERDAS Imagine Procedures. VII-6

Step 1:  Export VII-6


Emerge Process Document for the King County Department of Natural Resources

For Impervious Surfaces Distribution for Eastern King County

I.                   Introduction

The following instructions are the process steps taken by Marshall and Associates, Inc. (MARSHALL) in pre-processing and processing the Emerge product for the King County Department of Natural Resources (KCDNR). 

II.                Pre-processing

Goal 

One goal of the Pre-processing phase is to identify any problems with the data that might lessen the effectiveness of proposed techniques or render certain data unusable.  Other goals include geometric verification, and, if necessary, accurate data correction and different acquisition’s registration.

ERDAS Imagine Procedures

Step 1:  Impervious Surface Quality Assessment

1.1   Examine each acquisition for clouds, cloud shadows, haze, fog, smoke, excessive image striping or drop-outs, snow, ice, etc.

1.2   If any of the above are found in significant amounts, discard the acquisition and obtain an alternative acquisition.  If necessary, use an acquisition with clouds in it and eliminate the areas covered by clouds and cloud shadows from further consideration.

Step 2:  Emerge Image Access Satellite Overpass Mosaic

2.1   Mosaic images to panels.

2.2   Reproject Imagery into final desired projection.

2.3   Check for clouds.

2.4   Create the Normalized Difference Vegetation Index (NDVI) in float single format from channels 3 and 2 (Channel 3-Channel 2/Channel 3+Channel 2).

2.5   Digitize clouds by onscreen interpretation; low NDVI values can be used to help identify potential clouds.  Label all the pixels inside the AOI as UNKNOWN.

Step 3:  Positional Accuracy with Ancillary Datasets Verification

Compare ancillary datasets to imagery.

Potential ancillary datasets:

a)      GPS Points

b)      GIS Layers

c)      Other Imagery

Features such as road intersections, parcel boundaries, etc. can be used for visual verification.

III.              Impervious Strata

Goal

The goal of this phase is to spectrally stratify all of the pixels in the study area that are likely to be impervious, thus creating a file that enhances the contrast between impervious and pervious features.

ERDAS Imagine Procedures

Step 1:  NDVI Threshold

Threshold the NDVI to determine a spectral impervious stratum containing all impervious pixels (roads, sidewalks, roofs, etc.) with as few commission errors as possible (e.g. non-constructed bare surfaces).  The NDVI normalizes for the effects of shadows, reducing the differential spectral response of the sunlit and shadowed portions of the imagery.

Step 2:  Potential Thresholds Effectiveness Evaluation

Overlay the DNR training sets on the impervious stratum file.  Assess the proportion of pixels in each training set that are in the stratum.  Most of the pixels in the >80% impervious training sets should be included.

Step 4:  Impervious File Creation

For each mosaic, create a file that contains all of the pixels on the impervious side of the threshold (different thresholds for each image). 

Step 5:  Isodata Cluster

Cluster the impervious file with the Isodata algorithm (unsupervised classification).  Clustering options in ERDAS Imagine include ‘Initialize from the statistics,’ using the principle axis to initialize and scaling the range of signatures with ‘Automatic.’  The other parameters, such as number of clusters, number of processing iterations, and convergence threshold are determined on the basis of the image feature variability (in the case of Emerge, 130 clusters were set at 15 iterations with a convergence threshold of 0.950). 

Note:  There is a trade off in image analyst cost between creating too many clusters to be labeled and not creating enough clusters to identify all image features.

IV.            Unsupervised Classification

Goal

The goal of this phase is to refine the spectral impervious stratum created in phase III - Impervious Strata, ultimately removing spectral clusters consisting entirely of non-impervious and vegetative surfaces.

This activity can consume much time and energy if it is not properly limited.  One way to limit it is to assume that the DNR’s training sets are a representative sample to be categorized.  Given this assumption, tentative labels can be assigned to clusters based on the types of training sets with which they are most closely associated.

Procedures

Step 1:  Possible Pervious Clusters Identification

It is possible that one or more of the impervious stratum clusters could be uniquely associated with a pervious surface.  This proposition can be tested by observing whether any of the pixels associated with a cluster from the impervious stratum are found only in pervious category training sets and not in any of the impervious training sets.  Shadow may cause non-impervious features to look like some types of impervious.  If pixels in the impervious stratum occur in shadow, a representative sample should be analyzed to determine if they are true impervious or rather shadowed versions of pervious categories (e.g., forest and open water).  If they are true impervious, nothing needs to be done.

Step 2:  Category Labels Assignment

Assign the value of 1 for High Confidence impervious, 50 for Low Confidence Impervious, and 0 for other.  This also tracks error of commission, allowing for further revision either with ancillary data, heads up analysis, or cluster busting.

V.               Recovery from Spectral Aberrations Caused by Gamma Correction of Emerge Imagery (Addendum Above & Beyond)

Goal

The goal of this phase was to recover impervious features that have spectral properties (like pervious features) caused by gamma correction.  MARSHALL performed this addendum phase in addition to the scope of work to produce a better product for King County.

ERDAS Imagine Procedures

Step 1:  Isodata Cluster

Cluster the entire image with isodata (not just the impervious stratum).

Step 2:  Cluster Identification

2.1   Identify clusters associated with holes in the impervious coverage, paying special attention to minimizing commission error with pervious surfaces until a majority of the hole pixels have been identified (all pixels are not necessary).

2.2   Label these pixels as 50s.  Label all else as 0s (background).

Step 3:  File Recode

Recode this file.

Step 4:  File Appending

Append this file into the product of Phase VI/Step 2 Focal Filter.

VI.            Minimum Mapping Unit (MMU)

Goal

The goal of this phase is to produce recoded/refined clumps at the desired MMU, 0.0025 acres, regardless of impervious surface classification confidence.

ERDAS Imagine Procedures

Step 1:  File Recode

Create two files, recoding the cluster busted, labeled files to one file of 0s (background and pervious surfaces) 1s, and 50s and another file of 0s (background), 1s (impervious high and low confidences), and 2s (pervious surfaces, including those excluded from the impervious stratum).

Step 2:  Clump Elimination

Run the clump file through the ERDAS Elimination command and eliminate clumps seven pixels or less (determined by the desired MMU).

Step 3:  Focal Filter

This step is optional, pending step 1 results.

3.1   Do a 3x3 filter neighborhood analysis on the 1s and 2s file only, returning majority values to the center pixels (ignoring 2s in the focal window).  These filters strengthen feature identification, essentially filling in “holes” caused by saturated shadowed and aberrant features (e.g. painted objects) or noise in the sensor.

3.2   Repeat step 3.1.  The second filtering process is the maximum number of iterations possible without dramatically altering the Impervious Surface Area (ISA) distribution.

Step 4:  Clump

Clump the 1s and 2s file, using the ERDAS clump function, utilizing eight connected pixels.

Step 5:  Mask

Take the elimination file as a mask to the 0s, 1s, and 50s file, removing from it all cells that are not 1 on the 1 and 2 eliminated file.

This is accomplished by the following Boolean arguments:

5.1   If the product of step1 is >0 and the product of step 4=1, then return the value of step 1.

5.2   If the product of step 1=0 and the product of step 4=1, then return a value of 50.

5.3   If the product of step 4=2, then return a value of 0.

5.4   If the product of step 4=0, then return a value of 0.

VII.          Export Product

Goal

The goal of this phase is to export the final product into the specified format.

ERDAS Imagine Procedures

Step 1:  Export

1.1   Export the product of Phase VI – MMU to grid.

1.2   Setup attribute table.