1. Lecture 22: Remote Sensing Image Processing and Interpretation
------Using GIS--
Lecture 22:
Remote Sensing Image Processing and Interpretation
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

2. IMAGE ACQUISITION
IMAGE PROCESSING
(Feature extraction)
IMAGE CLASSIFICATION
ACCURACY ASSESSMENT

3. Image Pre-Processing Create a more faithful representation through:
Geometric correction
Radiometric correction
Atmospheric correction
Can also make it easier to interpret using “image enhancement”
Imagery can be ordered at different levels of correction and enhancement
Rectification – remove distortion (platform, sensor, earth, atmosphere) …. Scanned aerial photo vs. orthophoto

4. Image Enhancement
Image Enhancement: Improving the interpretability of the image by increasing apparent contrast among various features.
Contrast manipulation: Gray-level thresholding, level slicing, and contrast stretching.
Spatial feature manipulation: Spatial filtering, edge enhancement, and Fourier analysis.
Multi-image manipulation: Band ratioing, principal components, vegetation components, canonical components…. Orthophoto vs. “true” orthophoto
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

5. Image Enhancement: Contrast stretching
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

6. Spatial Feature Enhancement (local operation)
Spatial filtering/ Convolution:
Low-pass filter: emphasizes regional spatial trends, deemphasizes local variability
High-pass filter: emphasizes local spatial variability
Edge Enhancement: combines both filters to sharpen edges in image
See Raster processing lecture for review and examples of low- and high-pass filters.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

7. Image classification
This is the science of turning RS data into meaningful categories representing surface conditions or classes (feature extraction)
Spectral pattern recognition procedures classifies a pixel based on its pattern of radiance measurements in each band: more common and easy to use
Spatial pattern recognition classifies a pixel based on its relationship to surrounding pixels: more complex and difficult to implement
Temporal pattern recognition: looks at changes in pixels over time to assist in feature recognition
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

8. Spectral Classification
Two types of classification:
Supervised:
A priori knowledge of classes
Tell the computer what to look for
Unsupervised:
Ex post approach
Let the computer look for natural clusters
Then try to classify those based on posterior interpretation
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

9. Supervised Classification
Better for cases where validity of classification depends on a priori knowledge of the technician; already know what “types” you plan to classify
Conventional cover classes are recognized in the scene from prior knowledge or other GIS/ imagery layers
Training sites are chosen for each of those classes
Each training site “class” results in a cloud of points in n dimensional “measurement space,” representing variability of different pixels spectral signatures in that class
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

10. Supervised Classification
Here are a bunch of pre-chosen training sites of known cover type
Source: F.F. Sabins, Jr., 1987, Remote Sensing: Principles and Interpretation.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou
Source:

11. Supervised Classification
The next step is for the computer to assign each pixel to the spectral class is appears to belong to, based on the DN’s of its constituent bands
Clustering algorithms look at “clouds” of pixels in spectral “measurement space” from training areas to determine which “cloud” a given non-training pixel falls in.
Source: F.F. Sabins, Jr., 1987, Remote Sensing: Principles and Interpretation.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou
Source:

12. Supervised Classification
Algorithms include
Minimum distance to means classification (Chain Method)
Gaussian Maximum likelihood classification
Parallelpiped classification
Each will give a slightly different result
The simplest method is “minimum distance” in which a theoretical center point of point cloud is plotted, based on mean values, and an unknown point is assigned to the nearest of these. That point is then assigned that cover class.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

13. Supervised Classification
Introduction to GIS
Supervised Classification
Examples of two classifiers
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou
Source:

14. Unsupervised Classification
Assumes no prior knowledge
Computer groups all pixels according to their spectral relationships and looks for natural clusterings
Assumes that data in different cover class will not belong to same grouping
Once created, the analyst assesses their utility and can adjust clustering parameters
Spectral class 1
Spectral class 2
Source: F.F. Sabins, Jr., 1987, Remote Sensing: Principles and Interpretation.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

15. Spectral Classification
After comparing the reclassified image (based on spectral classes) to ground reference data, the analyst can determine which land cover type the spectral class corresponds to
Has advantage over supervised classification: the “classifier” identifies the distinct spectral classes, many of which would not have been apparent in supervised classification and, if there were many classes, would have been difficult to train all of them. Not required to make assumptions of what all the cover classes are before classification.
Clustering algorithms include: K-means, texture analysis
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

16. Spectral Classification
Fill in the blanks here
Unsupervised: example
Source:
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

17. Unsupervised Classification
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

18. Object-Oriented Classification
Traditional classifiers don’t work as well for new generation of high resolution data, like this 2 foot Emerge Color infrared airphoto. Why? Meaningless to classify each pixel
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

19. Object-Oriented Classification
Problems with pixel based classifiers:
Extreme heterogeneity of small pixels (e.g. shading, multiplicity of colors within an object)
Two pixels with same spatial reflectance might be totally different types of objects/ features (e.g. building and road)
Two pixels with very different reflectance may actually be part of the same object type (e.g. building materials of different reflectance)
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

20. Parking lot vs. building
Unfortunately traditional image processing techniques, those that attempt to classify a pixel based on its spectral reflectance, are poorly suited for classifying high-resolution imagery. Pixels that appear similar may be functionally very different. For example, ‘A’ is a parking lot and ‘B’ is a building, these features are spectrally similar but functionally different. Furthermore, classifying pixels does not allow us to take into account topological relationships. A tree pixel in the middle of a forest is functionally very different than a tree pixel in a downtown urban area.
Based on slide by Jarlath O’Neil Dunne
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

21. Parking lot vs. building
Image objects overcome the limiations of pixels by C
Based on slide by Jarlath O’Neil Dunne
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

22. Object-oriented classification: Step 1
Segmentation
Pixels turned into polygon “objects”
Minimize within-unit heterogeneity and maximize between unit heterogeneity, subject to some user defined parameters.
“Scale parameter” controls size of units
Can create a nested hierarchy of objects>>big objects containing smaller objects, containing smaller objects
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

23. Segmentation Introduction to GIS
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

24. Segmentation Introduction to GIS
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

25. Object-oriented classification: Step 2
Introduction to GIS
Object-oriented classification: Step 2
Feature Extraction
Following segmentation, each object is encoded with information about its tone, shape, area, context, neighborhors and spectral characteristics (e.g. mean, standard deviation, max, min or each band’s spectral reflectance); used to help discriminate objects
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

26. Object-oriented classification: Step 3
Introduction to GIS
Object-oriented classification: Step 3
Classification
Then objects are classified by either defining training areas of known cover type (known as supervised fuzzy classification) or creating class descriptions organized through inheritance-based rules into a knowledge base (known as fuzzy knowledge base classification).
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

27. Introduction to GIS
Classification
Knowledge base approach: complex membership functions can be derived that describe characteristics that are typical for a certain class. The more a given object displays the characteristics, the more likely it is to be classified into the class to which those characteristics pertain. Characteristics can be based on spectral response summary statistics, shape characteristics, adjacency, connectivity, and overlay with certain thematic features.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

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29. From segments to classes
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

30. From segments to classes
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

31. Classification hierarchies
The classification can be hierarchical and nested, with finer classifications within coarser ones
Small classified objects can be aggregated up to large object classes and large objects can be split into smaller ones. Can then assign different segmentations to different class hierarchy level
Allows for high precision classifications within coarser, general classifications
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

32. (c)2008 Lecture materials by Austin Troy and Weiqi Zhou

33. Classification with other layers
Can use additional thematic layers to populate the knowledge base and create rules about what a certain class can be on top of, next to, or near. This can increase the accuracy of classifications, especially as you increase categorical precision and start getting into classifiying land uses in addition to land cover
Hence, when you do training areas, you not only get average spectral responses and shape metrics for a class, but also can get average values from underlying layers to help increase classification accuracy
Examples: farm fields as fn of slope, soils, etc; different suburban development types as function of distance to urban centers, income, crime, etc.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

34. From image to classification
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

35. Object-oriented classification Software
eCognition: one of the top Object oriented classification software packages
More info: see Ecognition website:
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

36. Accuracy Assessment
This is one of the most important parts of image classification.
Error rates can be very high in classification accuracies, especially with lower resolution data, and where pixels are mixed
This is often the most time consuming part of image classification
NLCD effort undertook effort to classify errors in each type of land cover, broken down by region of the US
User’s accuracy for type X: Percent of pixels classified as X that really are X. Producer’s accuracy: percent of pixels that were classified as other than X but really are X.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

37. Accuracy Assessment:example
Table 1 Accuracy Assessment of LULC classification
Classified data
Reference data
Sum
User Acc. %
Urban
Forest
Other 71 13 16
100 2 10 88 73
123
104
300
Producer Acc. (%)
97.3
81.3
84.6
Overall accuracy (%)
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

38. Applications of image classification: Land Cover
Classes depend on the system being used. One of the most common is the USGS Anderson Classification System (Anderson et al. 1976). This classification scheme is hierarchical, with nine very general categories at Level I, and an increasing number of classes and detail and level increases. Paper available online at
Anderson system intermixes land use and land cover metrics, by inferring land use from land cover. Unfortunately, land cover can only tell us a limited amount about land use—think of outdoor recreation as a land use. Need additional data for these classes.
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

39. Land Cover/ Land Use Mapping
Land use and land cover classification system for use with remote sensor data (Anderson et al. 1976)
Level I Level II
1 Urban or Built-up Land 11 Residential
12 Commercial and Services
13 Industrial
14 Transportation, Communications, and Utilities
15 Industrial and Commercial Complexes
16 Mixed Urban or Built-up Land
17 Other Urban or Built-up Land
Level III
111 single family homes
112 multi family homes
115 mobile home parks
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

40. Land Cover/ Land Use Mapping
Level I Level II
2 Agricultural Land 21 Cropland and Pasture
22 Orchards, Groves, Vineyards, Nurseries, and Ornamental Horticultural Areas Confined Feeding Operations
24 Other Agricultural Land
3 Rangeland 31 Herbaceous Rangeland
32 Shrub and Brush Rangeland
33 Mixed Rangeland
4 Forest Land 41 Deciduous Forest Land
42 Evergreen Forest Land
43 Mixed Forest Land
5 Water Streams and Canals
52 Lakes
53 Reservoirs
54 Bays and Estuaries
6 Wetland Forested Wetland
62 Nonforested Wetland
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

41. Land Cover/ Land Use Mapping
Here is an example of LANDSAT data classified using the Anderson System
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou

42. Moving beyond traditional LULC
Think of all the ways that expert-based classification systems like object-oriented plus high-res imagery allow for new types of classification
Moving away from purely structural to more functional types of classification
(c)2008 Lecture materials by Austin Troy and Weiqi Zhou