Estimation of Crop Acreage
From Satellite Imagery
Daniel Kuntz
EENG 510 – Image Processing
Fall 2014
Overview of Project Goals
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Use satellite imagery to of the San Luis Valley to:
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Estimate where crops are and where crops are not in a given
scene
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Segment fields from each other so that the number of fields
and the area of each field can be determined
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Classify each field by crop
Why is this information useful?
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Market estimation, getting early estimation of crop yields
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Land management, legacy Landsat images can provide a
wealth of information about land use if they can be interpreted
effectively
Agriculture in the San Luis Valley
The San Luis Valley (SLV)
makes a good test case for
a proof of concept
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Fields are irrigated by
center pivot and are
mostly uniform circles with
a north / south road grid
running between them
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Climate is arid and fields
are not located next to
dense vegetation
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There are three main
crops: potatoes, barley
and alfalfa.
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Field Detection Approach
The first step in the process is to determine where the fields
are. The Normalized Difference Vegetation Index (NVDI) and
temporal averaging were used for this.
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The NVDI is defined as:
NVDI =
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r IR −r RED
, where: r x =reflectance in band x
r IR +r RED
For good results we need a temporal average of NVDI,
which we can then segment.
n
1
NVDI i=NVDI in each scene
NVDI AVG= ∑ NVDI i , where:
n i=1
n=number of scenes
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Field Detection Results
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Field Segmentation Approach
The best method for segmenting the fields while preserving
their area this particular case was to find the roads running
between the fields. This is done by:
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Determining a box which encompasses both roads and
fields, but not empty space:
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Finding candidate areas where the roads might be
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Morphological closing with a square element slightly bigger
than each field
Subtract the field area from the “box” area described above
Finding the roads
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Hough transform on the candidate areas
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Field Segmentation Results
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Field Segmentation Results: Good
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Field Segmentation Results: Bad
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Field Segmentation: Analysis
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We can get a big picture of how well the segmentation
works by calculating the percentage of unsegmented fields:
C = Number of fields with area > 700 (Multi-field blobs)
A = Total area of fields with area > 700
A
K =
(Number of fields represented by the blobs)
620
K
P =
(Percentage of under-segmented Fields)
N fields +( K−C )
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We can see that this technique could use a bit of refinement
N_fields
1272
C
72
A
92167
K
149
P
11.05
Classification Approach
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Classification by crop was attempted for each field using the
Support Vector Machine (SVM) classification method. Ground
truth data for each field was obtained from USGS CropScape
Website.
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Classification was attempted by using mean reflectance and
variance in reflectance in all 7 bands along with the month
of the image.
Because of time and computational restraints, a subset of
96 fields was chosen for classification
Chosen classification groups were: Potatoes, Barley, Alfalfa,
Potatoes/Barely, and Other.
Classification Approach
Method for obtaining result:
1) Set was divided randomly into 80% training and 20% test
datasets
2) SVM was trained with the 80% of data and tested with the
other 20%
3) Step 1-2 was repeated 100 times and the mean and variance
of all outcomes was taken.
4) After all trials had been run, the mean and variance of the
result for each trial was recorded
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Classification Results
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Results achieved for crop classification were about 48% of
fields properly identified, (much better than randomly
guessing). Given the small dataset of about 1260 data
points, and 96 fields, it is indicated that this method may be
feasible with more work.
Computer Program Output:
After 100 trials, correct outcome stats:
Mean: 0.48190476190476184
Var:
0.0009165784832451503
Aside: USGS Classifications
2008 Classifications
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2012 Classifications
Conclusion / Possible Improvements
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Overall Outcome
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Results of this project are not very impressive, but given the
large amount of room for improvement, it proves the feasibility
of something similar on a larger scale
Possible Improvements
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Using a “watershed” segmentation algorithm may provide a
better, more general way to segment the fields.
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A much larger dataset would be very beneficial.
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Some way to gauge the start of the growing season would
reduce the variability in phrenology of crops
Questions
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Please ask some!
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