1. Post Processing Of digitally classified imagery…

2. Post Processing Why process any further? Spatial Filtering Pixels to Polygons Topics:

3. Post Processing Four main reasons to process further: 1.Manually Edit the Classification  e.g. steep, deeply shaded slopes classified as water..., 2.Refine the classification using ancillary data,  subject of a subsequent chapter, 3.Summarize (smooth) the classification, 4.Convert the classified data to vector format. Why Process Any Further?

4. Why Process Further? The vector format has advantages in certain applications: Set by pixel size Variable Disk Storage: Determined by pixel Usually more and image size efficient Limit of Spatial Accuracy: Within 1 pixel Unlimited Spatial Analysis... - - possible operations: Same -computational speed: Usually Greater Usually Less - - topology: No explicit topology Explicit topology Raster Vector Resolution:

5. Post Processing In addition:  Users usually find vector format output easier to interpret and,  Other data may already be in vector format, There are Disadvantages:  Further processing requires time, money and disk space,  May alter class boundaries How are pixels converted to polygons...? Pixels to Polygons...

6. Raster and Vector Integration Polygons based on pixel classification,  Automatic conversion to polygons isn’t feasible because of too many tiny polygons, Filters are used to aggregate pixels into more homogenous groupings, Automatic conversion of the aggregated pixel groupings to polygons. Creating Polygons from Pixels...

7. Post Processing--Spatial Filtering Spatial Filtering is an area operator. It creates new map values as a function of values of existing neighboring pixels, Usually summarizes the existing map using a “roving window” or kernel, Standard kernel operators:  Maximum, minimum, majority, minority, mean, median, mode, standard deviation, diversity and many more.

8. Moving Window Concept

18. Example: Mean Kernel Calculation

19. Example: Mode Kernel Calculation

20. Mode Kernel Example The Original Classification: Note the “salt and pepper” appearance (high spatial frequency)…

21. Mode Kernel Example The Mode-Filtered Output: Note the “salt and pepper” appearance (high spatial frequency) is much less.

22. Pixels to Polygons The aggregated pixels can be automatically converted to polygons…

23. Pixels to Polygons The polygons alone…

24. Pixels to Polygons The variability within polygons is not lost -- the polygons can be displayed with the original classification.

25. Raster and Vector Can use existing polygons, Summarize pixel class values, Indicates the intra-polygon variation. Database Integration:

26. Raster and Vector Database Integration: Composition of Polygon 47 class acres % description 7 30 15 crown closure 10 - 40% 8 60 30 crown closure 41 - 70% 9 110 55 crown closure 71 -100% 47