remote sensing part 4 classification & vegetation indices
TRANSCRIPT
Remote SensingPart 4
Classification & Vegetation Indices
Classification Introduction• Humans are classifiers by nature - we’re always
putting things into categories
• To classify things, we use sets of criteria
• Examples: – Classifying people by age, gender, race, job/career, etc.– Criteria might include appearance, style of dress, pitch of
voice, build, hair style, language/lexicon, etc.– Ambiguity comes from:
• 1) our classification system (i.e., what classes we choose) • 2) our criteria (some criteria don’t differentiate people with
complete accuracy)• 3) our data (i.e., people who fit multiple categories and people
who fit no categories)
Non-Remote Sensing Classification Example
• “Sorting incoming Fish on a conveyor according to species using optical sensing”
Sea bassSpecies
Salmon
** The following data are just hypothetical
Methods
– Set up a camera and take some sample images to extract features
• Length• Lightness• Width• Number and shape of fins• Position of the mouth, etc…
Scanning the Fish
• Classification #1
– Use the length of the fish as a possible feature for discrimination
• Fish length alone is a poor feature for classifying fish type– Using only length we would be correct 50-60% of the time– That’s not great because random guessing (i.e., flipping a
coin) would be right ~50% of the time if there are an equal number of each fish type
• Classification #2
– Use the lightness (i.e., color) of the fish as a possible feature for discrimination
• Fish lightness alone is a pretty good feature for classifying fish by type– Using only lightness we would be correct ~ 80% of
the time
• Classification #3
– Use the width & lightness (i.e., color) of the fish as possible features for discrimination
• Fish lightness AND fish width do a very good job of classifying fish by type– Using lightness AND width we would be correct
~90% of the time
How does this relate to remote sensing?
• Instead of fish types, we are typically interested in land cover– For example: forests, crops, urban areas
• Instead of fish characteristics we have reflectance in the spectral bands collected by the sensor– For example: Landsat TM bands 1-6 instead
of fish length, width, lightness, etc.
Imagery Classification
• Two main types of classification– Unsupervised
• Classes based on statistics inherent in the remotely sensed data itself
• Classes do not necessarily correspond to real world land cover types
– Supervised• A classification algorithm is “trained”
using ground truth data• Classes correspond to real world land
cover types determined by the user
Notes
• For ease of display the following examples show just 2 bands: – one band on the X-axis– one band on the Y-axis
• In reality computers use all bands when doing classifications
• These types of graphs are often called feature space
• The points displayed on the graphs relate to pixels from an image
• The term cloud sometimes refers to the amorphous blob(s) of pixels in the feature space
Unsupervised Classification• Classes are created based on the
locations of the pixel data in feature space
Red BV’s
Infrared BV’s
0
0 255
255
v
A Computer Algorithm Finds Clusters
Red BV’s
Infrared BV’s
0
0 255
255
v
Unsupervised Classification
Unsupervised Classification
• Attribution phase – performed by human
water
Soil
agriculture
forest
Red BV’s
Infrared BV’s
0
0 255
255
Problems with Unsupervised Classification
Red BV’s
Infrared BV’s
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0 255
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v
The computer may consider these 2 clusters (forest and agriculture) as one cluster The computer may consider
this cluster (soil) to be 2 clusters
Supervised Classfication• We “train” the computer program using ground truth data
• I.e., we tell the computer what our classes (e.g., trees, soil, agriculture, etc.) “look like”
Coniferous treesDeciduous trees
Supervised Classification
Red BV’s
Infrared BV’s
0
0 255
255
v
Sample pixels
Other pixels
Supervised Classification• No attribution phase necessary
because we define the classes before-hand
water
Soil
agriculture
forest
Red BV’s
Infrared BV’s
0
0 255
255
Problems with Supervised Classification
Red BV’s
Infrared BV’s
0
0 255
255
vforest
agri
water
Soil
What’s this?
v
What is the computer actually doing?
• This classification generates statistics for the center, the size, and the shape of the sample pixel clouds
• The computer will then classify all the rest of the pixels in the image using these statistical values
Example: Remote Sensing of Clouds
Supervised Classification: Training Samples
• Users survey (using GPS) areas of “pure” land cover for all possible land cover types in an image
• OR
• Users “heads-up” digitize “pure” areas using expert knowledge and/or higher spatial resolution imagery
• The rest of the image is classified based on the spectral characteristics of the training sites
Classification of Nang Rong imagery
(a) Nov 1979
(c) Nov 2001
Shown are Landsat MSS,TM,and ETM Image Classification Results
(a) Nov 1992
Upland Ag
Forest
Rice
Water
Built-up
Land Use/cover Change in Nang Rong, Thailand
1954 1994
Example Classification Results (Bangkok, Thailand)
Accuracy Assessments
• After classifying an image we want to know how well the classification worked
• To find out we must conduct an accuracy assessment
How are accuracy assessments done?
• Basically we need to compare the classification results with real land cover
• As with training data, the real land cover data can be field data (best) or samples from higher spatial resolution imagery (easier)
• What points should we use for the accuracy assessment?– Possible options (there are others)
• Random points• Stratified random points (each class represented with an
equal number of points)
Classification Challenges
• What problem might occur when gathering points for an accuracy assessment (and to a lesser extent, training areas)?
• Can we use the same points for the accuracy assessment that we used to train the classification?
Ikonos Imagery: Glacier National Park
Classification Results
Accuracy Assessment Table
• Rows are the reference data, columns are the classified data
• Values on the diagonal are correctly classified
• The values in red are the producer’s accuracy for each class– A.k.a. errors of omission– E.g., “how many pixels that ARE water (13) are classified AS water (12)”
• The values in blue are the user’s accuracy for each class– A.k.a. the errors of commission– E.g., “how many pixels classified AS water (14) ARE water (12)”
• Overall accuracy = # of correctly classified pixels / total # of pixels
• The Kappa statistic is basically the overall accuracy adjusted for how many pixels we would expect to correctly classify by chance alone
Vegetation Indices
Vegetation Indices
• Normalized Differential Vegetation Index (NDVI)
• Takes advantage of the “red edge” of vegetation reflectance that occurs between red and near infrared reflectance (NIR)
• NDVI = (NIR – Red) / (NIR + Red)
• Many more indices with many variants exist (lots of acronyms like SAVI, etc.)
Normalized Difference Vegetation Index (NDVI)
dNIR
dNIR
RR
RRNDVI
Re
Re
NDVI: [-1.0, 1.0]
Often, the more leaves of vegetation there are present, the bigger the contrast in reflectance in the red and near-infrared spectra
NDVI most accurately approximates the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)
NDVI from AVHRR
Feb 27-Mar 12
Jul 17-Jul 30
Aug 14-Aug 27
Jun 19-Jul 2
Apr 24-May 7
Nov 6- Nov19
NDVI and Precipitation Relationships
Expansion and contraction of the Sahara
A: 12 Apr-2 May 1982B: 5 to 25 Jul 1982C: 22 Sep to 17 Oct 1982D: 10 Dec 1982-9Jan 1983
Monitoring forest fire
Pre-forest fire
Post-forest fire
Burned area identified from space using NDVI