1. Incorporating Ancillary Data for Classification
Beyond spectral classification

2. Wyoming Elevation Data

3. Quick review of classification
Unsupervised vs. supervised
Spectral vs. informational classes
Spectral confusion
Requirements for accurately distinguishing classes
Etc.

4. Ancillary Data -- Definition
“Data acquired by means other than remote sensing that are used to assist in the classification or analysis of remotely sensed data.” (Campbell 2002)
Sometimes called collateral data.
More broadly, we can include non-spectral data derived from remotely sensed data (e.g., image texture, etc.), along with other kinds of data (e.g. elevation)

5. Examples of ancillary data
Elevation data and derived products (categorical or continuous)
Elevation, slope, aspect, topographic position, curvature
Soil data (categorical or continuous)
Soil type, salt content, soil depth, soil texture, etc.
Geologic data (categorical or continuous)
Surface geology, bedrock geology, rock type, chemical constituents, etc.
Geomorphic data
Climate data
Hydrologic data
Etc.

6. Hillshading from digital elevation model – can capture solar irradiance effects and sometimes improve classification

7. Accurate, fine-scale soils maps: The Holy Grail of land cover mapping.

8. Goshen County, Wyoming
Soils maps for large areas are usually not detailed enough to substantially help with vegetation classification

10. Existing vegetation data can constrain new classifications or be used as ancillary data for predicting geology or other themes

11. Opportunities afforded by ancillary data
Discriminate types that are not spectrally unique
Incorporate knowledge about physical processes that control the distribution of features on earth
Better understand earth systems by exploring our ability to capture the drivers that control their spatial distributions
Explore mismatches between predictions of where things should be and where they really are

12. Resolving spectral confusion with ancillary data
Elevation
Spectral Info

13. Challenges in using ancillary data
Key ancillary data are often missing or incomplete
Ancillary data can be logically incompatible with remotely sensed data
Ancillary data types and formats may not match those of remotely sensed data and therefore violate statistical assumptions (e.g., continuous vs. categorical variables, non-normal distributions, etc.)
Must use appropriate methods for combining ancillary data with spectral data

14. Some common techniques for incorporating ancillary data
A priori stratification of the map area before spectral classification
Inclusion of ancillary data as an additional band or channel in the classification
Use ancillary data to modify prior (a priori) probabilities in a maximum likelihood classification
Post classification sorting of spectral results
Use of non-parametric techniques like decision trees

15. Stratifying with ancillary data
Use ancillary data to divide the spectral classification into logical parts and classify each separately
Recognizes that same spectral classes might be different information classes (e.g., shortgrass prairie vs. tall grass prairie) in different geographic areas.
Problems can arise due to:
Gradients between strata being cut by hard boundaries
Differences in classification across strata boundaries for same type (edge matching)
Mismatch in scale of strata vs. image data

16. Elevation strata – can stratify by cutoffs, but elevation is really a gradient
(Figure from Ghorbani et al.)

17. Adding ancillary data as an additional channel
Ancillary data added as an additional band before classification
Must consider data type – can’t logically combine categorical data with continuous (e.g., spectral) data if you are using parametric classification.
Must also consider data range (e.g., NDVI [-1 to 1] combined with 8-bit [0-255] data can cause inappropriately large or small influence of the ancillary data on the outcome

18. Modifying prior probabilities
Prior probability is an estimate of the statistical distribution of objects that fall into a particular class
Can alter the probability distribution (change the standard deviation) for specific classes based on ancillary data
Increases or decreases the chance of an unknown pixel being grouped with a particular training class (Strahler 1980)
Objects (pixels) are forced more readily into classes with higher probabilities

20. Post-classification sorting
Classify an area and develop an error matrix based on comparison of ground data to classified data
Isolate the types of confusion occurring in the map (off diagonal elements of the matrix)
Target ancillary data that could help correct the confused types

21. Contingency Table Reference data Classified image Forest Urban Total
Water
Forest
Urban
Total 21 5 7 33 6 31 2 39 1 22 23 27 37 95
What ancillary data might help distinguish water from urban or water from forest?

22. Non-parametric methods
Sometimes called layered or multi-step classification
Allow logical combination of disparate data types in a way that incorporates implicitly some of the techniques discussed today (e.g., decision trees in some ways stratify a classification)

23. Decision tree for impervious surface mapping
Decision tree for impervious surface mapping. (University of Maryland RESAC)

24. Summary: Ancillary data
Ancillary data are a powerful tool for improving classification accuracy
Must be incorporated thoughtfully
Knowledge of the systems being mapped and the drivers that control spatial distribution
Careful treatment of multiple data types in ways that do not violate statistical assumptions
Care not to “overfit” your classification by using very training-site-specific ancillary data