Image Classification Using Multi-spectral And Multi-temporal Satellite Data
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Image Classification using multi-spectral and multi-temporal satellite data
Project by: Mr. Vinod Jadhav Ms. Deepali Gadkari Ms. Sangita Warade
Approach Introduction Aims & objects Methodology Observations & Results Conclusion
Introduction The project is divided in two parts: – Image classification – Change detection
Introduction
Image Classification
It is replacement of visual analysis of the image data with quantitative techniques for automating the identification of features in a scene. It categorizes all pixels in an image into land cover classes. It distinguishes as supervised and unsupervised classification.
Introduction
Image Classification
In supervised the image analyst supervises the pixel categorization process by specifying training areas. In unsupervised the image data are first classified by aggregating them into the natural spectral groupings, or clusters, present in scene.
Introduction
Change Detection
Change Detection involves the use of multitemporal data sets to – –
discriminate areas of land cover change between dates of imaging. Change in identify crop health pattern
Aims & objects Supervised and unsupervised classification of multi-spectral data for land-use mapping To decide which classifier is better for Landuse / Landcover mapping – By comparing statistical area – By pixel-by-pixel comparison
Change detection by using multi-temporal data
Study Area Area of interest: –Location: Part of Patna, Gaya and Aurangabad
Districts of Bihar
Patna Town
Study Area FCC IRS-1D LISS-III February-2002
Son River
Hills
Methodology Image classification by applying Supervised & Unsupervised methods Supervised Classification – Subsetted LISS-III image – Image enhancement – Collection of training sets – Applications of different classifiers • Minimum Distance • Maximum Likelihood
Methodology Quantitative Expressions of category separation Signature Separability Scatter Plots Area PCT values Confusion matrix
Application (Running) of Classification
Methodology Unsupervised Classification – Defining number of classes – Applications of K-mean classifier
Source of Data A) Image Classification: Satellite:
IRS-1D
Sensor:
LISS-III
Spatial Resolution:
23.5 meters
Spectral Resolution: • Green: 0.52-0.59mm • Red:
0.62 - 0.86 mm
• Near Infrared: 0.77 - 0.86 mm • Middle Infrared: 1.55 - 1.70 mm
Date of image: February 2002
Source of Data B) Change Detection: Satellite:
IRS-1D
Sensor:
WiFS
Spatial Resolution:
188 meters
Spectral Resolution: • Red:
0.62 - 0.86 mm
• Near Infrared: 0.77 - 0.86 mm
Dates of image: • January 2000 • February 2000
Software Geomatica 8.2.1 – Focus – PCI Modeler – OrthoEngine Core
Classifications Unsupervised Classification – K-mean Classification (5,10,15,20 Classes) – Fuzzy – ISODATA
Supervised Classification – Maximum Likelihood Classifier – Minimum Distance Classifier
Unsupervised –5 Classes
Unsupervised –10 Classes
Unsupervised –15 Classes
Unsupervised –20 Classes
Unsupervised –Fuzzy
Unsupervised –ISODATA
Unsupervised: 12 Classes
Unsupervised: 6 Classes(Aggregated)
Supervised Classification Classes (Training sites) – – – – – – –
Agriculture-1 Agriculture-2 River Sand Fallow Land Hills Settlement
Supervised –Min Distance
Supervised –Max Likelihood
Supervised –Parallepiped
Supervised –Parallepiped with Tie breaker
Parameters used for Classification Interpretation Scatterplot Signature Separability Area PCT values Confusion matrix
Scatter Plot: Minimum distance classification with default threshold
Agriculture2 Sand
Hills
River
Fallow Land Settlements
Scatter Plot: Minimum distance classification while applying threshold
Fallow Land Settlements
Scatter Plot: Minimum distance classification after applying threshold
Agriculture2 Sand
Hills Fallow Land
River Settlements
Signature Separability: Minimum distance classification
Minimum Distance Classification: Pixel distribution
Name
Code
Pixels
Image
Thres
Bias
Agriculture-1
1
0
0.00
3.00
1.00
Agriculture-2
2
1639264
32.23
3.00
1.00
River
3
44893
0.88
3.00
1.00
Sand
4
81202
1.60
3.00
1.00
Settlements
5
711024
13.98
3.00
1.00
Fallow Land
6
2508629
49.32
3.00
1.00
Hills
7
101088
1.99
3.00
1.00
0
0
0.00
Total
5086100
100.00
NULL
Minimum Distance Classification: Confusion Matrix Name
Code
Pixels
2
3
4
5
6
7
-------------------------------------------------------------------------Agriculture-2
2
583
99.31
0.00
0.00
0.00
0.69
0.00
River
3
837
0.00
83.03
2.63
13.14
0.12
1.08
Sand
4
999
0.00
0.00
98.80
1.20
0.00
0.00
Settlements
5
1080
0.00
0.00
0.00
92.96
5.46
1.57
Fallow Land
6
676
0.00
0.00
0.00
11.83 68.93
19.23
Hills
7
578
0.00
0.00
0.00
21.28 14.01
64.71
Average accuracy = 84.63 Overall accuracy = 86.37
KAPPA COEFFICIENT = 0.83360 Standard Deviation = 0.00605 Confidence Level : 99 +/- 0.01560 95 +/- 0.01185 90 +/- 0.00995
Scatter Plot: Maximum Likelihood Classification with default threshold
Agriculture2 Sand Hills River Fallow Land
Settlements
Scatter Plot: Maximum Likelihood Classification while applying threshold values
Fallow Land Settlements
Scatter Plot: Maximum Likelihood Classification after applying threshold
Agriculture2 Hills
Sand
Fallow Land
River Settlements
Signature Separability: Maximum Likelihood classification
Maximum Likelihood Classification: Pixel Distribution Name
Code
Pixels
Image
Thres
Bias
Agriculture-1
1
3396814
66.79
3.00
1.00
Agriculture-2
2
632902
12.44
3.00
1.00
River
3
35026
0.69
3.00
1.00
Sand
4
40182
0.79
3.00
1.00
Settlements
5
142596
2.80
3.00
1.00
Fallow Land
6
746469
14.68
3.00
1.00
Hills
7
92111
1.81
3.00
1.00
0
0
0.00
NULL Total
5086100 100.00
Maximum Likelihood Classification: Confusion Matrix Name
Code
Pixels
1
2
3
4
5
6
7
-------------------------------------------------------------------------Agriculture-2
2
583
7.55
92.45
0.00
0.00
0.00
0.00
0.00
River
3
837
14.22
0.00
85.78
0.00
0.00
0.00
0.00
Sand
4
999
5.91
0.00
0.00
94.09
0.00
0.00
0.00
Settlements
5
1080
5.19
0.00
0.65
0.00
90.09
1.94
2.13
Fallow Land
6
676
2.81
0.00
0.00
0.00
0.74
83.58
12.87
Hills
7
578
4.67
0.00
0.00
0.00
1.90
6.23
87.20
Average accuracy = 88.87 Overall accuracy = 89.19
KAPPA COEFFICIENT = 0.87065 Standard Deviation = 0.00530
Confidence Level : 99 +/- 0.01367 95 +/- 0.01038 90 +/- 0.00871
Scatter Plot: Maximum Likelihood Classification with default threshold values
Agriculture2 Hills Sand
Fallow Land
River Settlements
Scatter Plot: Maximum Likelihood Classification while applying threshold
Fallow Land Settlements
Scatter Plot: Maximum Likelihood Classification after applying threshold
Agriculture2 Hills
Sand
Fallow Land
River Settlements
Signature Separability: Maximum Likelihood classification
Comparison of Maximum Likelihood & Minimum Distance classification
Pixel-by-pixel Method
Comparison of Maximum Likelihood & Minimum Distance classification
Pixel-by-pixel Method
Area wise Comparison of classifications (In %) Supervised Unsupervised Max. Likelihood Min. Distance K-Means Agriculture-1 66.79 0 49.04 Agriculture-2 12.44 32.23 20.30 River 0.69 0.88 1.43 Sand 0.79 1.6 0.47 Settlements 2.8 13.98 7.73 Fallow Land 14.68 49.32 12.29 Hills 1.81 1.99 8.74
Observations Area under each class is another criteria to compare two classifiers and two classification schemes.
Conclusion MXL gives better results than Min.Dist. Supervised Classification is giving better results than Unsupervised classification Settlements,Hills and Fallow land are the classes with close reflectance patterns. Band combination,threshold value, bias and tolerance are of critical importance for Separability of closely resembling classes.
Change Detection WiFS-Image1 - NIR WiFS-Image1 - R NDVI-1 = (NIR-R) / (NIR+R) WiFS-Image2 - NIR WiFS-Image2 - R NDVI-2 = (NIR-R) / (NIR+R) Image Subtraction = (NDVI-1) - (NDVI-2)
Modeler for NDVI of image-1 & image- 2 NDVI = (Near Infra Red – Red) / (Near Infra Red + Red)
Resultant of image-1: NDVI-1
Resultant of image-2: NDVI-2
Modeler for Change Detection
Resultant of image-1:(NDVI-1) – (NDVI-2)
Classified (NDVI-1) – (NDVI-2)
Statistics (NDVI-1) – (NDVI-2) (Feb., 2000 – Jan. 2000) Classification Algorithm: K-Means Unsupervised Classification Input Channels: 1 Classification Result Channel: 2 Number of Clusters: 5 Cluster
Pixels
( 1)
26280
-0.10982
0.07458
( 2)
125943
0.04669
0.03788
( 3)
261951
0.15975
0.03032
254666 156760 -----------Total 825600
0.26006 0.38541
0.03207 0.04524
( 4) ( 5)
Mean Position Std Dev
Observations & Results Differences in NDVIs ranges from – 0.109 to 0.385 96.82% area is classified under positive values. 3.18% area is classified under negative values. i.e. crop was having peak vegetative growth in the month of Jan. and reached maturity in the month of Feb.
Conclusion NDVI of multi-date satellite data may be used to separate different crops / crop growth stages.
Thank You !
Project by: Mr. Vinod Jadhav Ms. Deepali Gadkari Ms. Sangita Warade
Approach Introduction Aims & objects Methodology Observations & Results Conclusion
Introduction The project is divided in two parts: – Image classification – Change detection
Introduction
Image Classification
It is replacement of visual analysis of the image data with quantitative techniques for automating the identification of features in a scene. It categorizes all pixels in an image into land cover classes. It distinguishes as supervised and unsupervised classification.
Introduction
Image Classification
In supervised the image analyst supervises the pixel categorization process by specifying training areas. In unsupervised the image data are first classified by aggregating them into the natural spectral groupings, or clusters, present in scene.
Introduction
Change Detection
Change Detection involves the use of multitemporal data sets to – –
discriminate areas of land cover change between dates of imaging. Change in identify crop health pattern
Aims & objects Supervised and unsupervised classification of multi-spectral data for land-use mapping To decide which classifier is better for Landuse / Landcover mapping – By comparing statistical area – By pixel-by-pixel comparison
Change detection by using multi-temporal data
Study Area Area of interest: –Location: Part of Patna, Gaya and Aurangabad
Districts of Bihar
Patna Town
Study Area FCC IRS-1D LISS-III February-2002
Son River
Hills
Methodology Image classification by applying Supervised & Unsupervised methods Supervised Classification – Subsetted LISS-III image – Image enhancement – Collection of training sets – Applications of different classifiers • Minimum Distance • Maximum Likelihood
Methodology Quantitative Expressions of category separation Signature Separability Scatter Plots Area PCT values Confusion matrix
Application (Running) of Classification
Methodology Unsupervised Classification – Defining number of classes – Applications of K-mean classifier
Source of Data A) Image Classification: Satellite:
IRS-1D
Sensor:
LISS-III
Spatial Resolution:
23.5 meters
Spectral Resolution: • Green: 0.52-0.59mm • Red:
0.62 - 0.86 mm
• Near Infrared: 0.77 - 0.86 mm • Middle Infrared: 1.55 - 1.70 mm
Date of image: February 2002
Source of Data B) Change Detection: Satellite:
IRS-1D
Sensor:
WiFS
Spatial Resolution:
188 meters
Spectral Resolution: • Red:
0.62 - 0.86 mm
• Near Infrared: 0.77 - 0.86 mm
Dates of image: • January 2000 • February 2000
Software Geomatica 8.2.1 – Focus – PCI Modeler – OrthoEngine Core
Classifications Unsupervised Classification – K-mean Classification (5,10,15,20 Classes) – Fuzzy – ISODATA
Supervised Classification – Maximum Likelihood Classifier – Minimum Distance Classifier
Unsupervised –5 Classes
Unsupervised –10 Classes
Unsupervised –15 Classes
Unsupervised –20 Classes
Unsupervised –Fuzzy
Unsupervised –ISODATA
Unsupervised: 12 Classes
Unsupervised: 6 Classes(Aggregated)
Supervised Classification Classes (Training sites) – – – – – – –
Agriculture-1 Agriculture-2 River Sand Fallow Land Hills Settlement
Supervised –Min Distance
Supervised –Max Likelihood
Supervised –Parallepiped
Supervised –Parallepiped with Tie breaker
Parameters used for Classification Interpretation Scatterplot Signature Separability Area PCT values Confusion matrix
Scatter Plot: Minimum distance classification with default threshold
Agriculture2 Sand
Hills
River
Fallow Land Settlements
Scatter Plot: Minimum distance classification while applying threshold
Fallow Land Settlements
Scatter Plot: Minimum distance classification after applying threshold
Agriculture2 Sand
Hills Fallow Land
River Settlements
Signature Separability: Minimum distance classification
Minimum Distance Classification: Pixel distribution
Name
Code
Pixels
Image
Thres
Bias
Agriculture-1
1
0
0.00
3.00
1.00
Agriculture-2
2
1639264
32.23
3.00
1.00
River
3
44893
0.88
3.00
1.00
Sand
4
81202
1.60
3.00
1.00
Settlements
5
711024
13.98
3.00
1.00
Fallow Land
6
2508629
49.32
3.00
1.00
Hills
7
101088
1.99
3.00
1.00
0
0
0.00
Total
5086100
100.00
NULL
Minimum Distance Classification: Confusion Matrix Name
Code
Pixels
2
3
4
5
6
7
-------------------------------------------------------------------------Agriculture-2
2
583
99.31
0.00
0.00
0.00
0.69
0.00
River
3
837
0.00
83.03
2.63
13.14
0.12
1.08
Sand
4
999
0.00
0.00
98.80
1.20
0.00
0.00
Settlements
5
1080
0.00
0.00
0.00
92.96
5.46
1.57
Fallow Land
6
676
0.00
0.00
0.00
11.83 68.93
19.23
Hills
7
578
0.00
0.00
0.00
21.28 14.01
64.71
Average accuracy = 84.63 Overall accuracy = 86.37
KAPPA COEFFICIENT = 0.83360 Standard Deviation = 0.00605 Confidence Level : 99 +/- 0.01560 95 +/- 0.01185 90 +/- 0.00995
Scatter Plot: Maximum Likelihood Classification with default threshold
Agriculture2 Sand Hills River Fallow Land
Settlements
Scatter Plot: Maximum Likelihood Classification while applying threshold values
Fallow Land Settlements
Scatter Plot: Maximum Likelihood Classification after applying threshold
Agriculture2 Hills
Sand
Fallow Land
River Settlements
Signature Separability: Maximum Likelihood classification
Maximum Likelihood Classification: Pixel Distribution Name
Code
Pixels
Image
Thres
Bias
Agriculture-1
1
3396814
66.79
3.00
1.00
Agriculture-2
2
632902
12.44
3.00
1.00
River
3
35026
0.69
3.00
1.00
Sand
4
40182
0.79
3.00
1.00
Settlements
5
142596
2.80
3.00
1.00
Fallow Land
6
746469
14.68
3.00
1.00
Hills
7
92111
1.81
3.00
1.00
0
0
0.00
NULL Total
5086100 100.00
Maximum Likelihood Classification: Confusion Matrix Name
Code
Pixels
1
2
3
4
5
6
7
-------------------------------------------------------------------------Agriculture-2
2
583
7.55
92.45
0.00
0.00
0.00
0.00
0.00
River
3
837
14.22
0.00
85.78
0.00
0.00
0.00
0.00
Sand
4
999
5.91
0.00
0.00
94.09
0.00
0.00
0.00
Settlements
5
1080
5.19
0.00
0.65
0.00
90.09
1.94
2.13
Fallow Land
6
676
2.81
0.00
0.00
0.00
0.74
83.58
12.87
Hills
7
578
4.67
0.00
0.00
0.00
1.90
6.23
87.20
Average accuracy = 88.87 Overall accuracy = 89.19
KAPPA COEFFICIENT = 0.87065 Standard Deviation = 0.00530
Confidence Level : 99 +/- 0.01367 95 +/- 0.01038 90 +/- 0.00871
Scatter Plot: Maximum Likelihood Classification with default threshold values
Agriculture2 Hills Sand
Fallow Land
River Settlements
Scatter Plot: Maximum Likelihood Classification while applying threshold
Fallow Land Settlements
Scatter Plot: Maximum Likelihood Classification after applying threshold
Agriculture2 Hills
Sand
Fallow Land
River Settlements
Signature Separability: Maximum Likelihood classification
Comparison of Maximum Likelihood & Minimum Distance classification
Pixel-by-pixel Method
Comparison of Maximum Likelihood & Minimum Distance classification
Pixel-by-pixel Method
Area wise Comparison of classifications (In %) Supervised Unsupervised Max. Likelihood Min. Distance K-Means Agriculture-1 66.79 0 49.04 Agriculture-2 12.44 32.23 20.30 River 0.69 0.88 1.43 Sand 0.79 1.6 0.47 Settlements 2.8 13.98 7.73 Fallow Land 14.68 49.32 12.29 Hills 1.81 1.99 8.74
Observations Area under each class is another criteria to compare two classifiers and two classification schemes.
Conclusion MXL gives better results than Min.Dist. Supervised Classification is giving better results than Unsupervised classification Settlements,Hills and Fallow land are the classes with close reflectance patterns. Band combination,threshold value, bias and tolerance are of critical importance for Separability of closely resembling classes.
Change Detection WiFS-Image1 - NIR WiFS-Image1 - R NDVI-1 = (NIR-R) / (NIR+R) WiFS-Image2 - NIR WiFS-Image2 - R NDVI-2 = (NIR-R) / (NIR+R) Image Subtraction = (NDVI-1) - (NDVI-2)
Modeler for NDVI of image-1 & image- 2 NDVI = (Near Infra Red – Red) / (Near Infra Red + Red)
Resultant of image-1: NDVI-1
Resultant of image-2: NDVI-2
Modeler for Change Detection
Resultant of image-1:(NDVI-1) – (NDVI-2)
Classified (NDVI-1) – (NDVI-2)
Statistics (NDVI-1) – (NDVI-2) (Feb., 2000 – Jan. 2000) Classification Algorithm: K-Means Unsupervised Classification Input Channels: 1 Classification Result Channel: 2 Number of Clusters: 5 Cluster
Pixels
( 1)
26280
-0.10982
0.07458
( 2)
125943
0.04669
0.03788
( 3)
261951
0.15975
0.03032
254666 156760 -----------Total 825600
0.26006 0.38541
0.03207 0.04524
( 4) ( 5)
Mean Position Std Dev
Observations & Results Differences in NDVIs ranges from – 0.109 to 0.385 96.82% area is classified under positive values. 3.18% area is classified under negative values. i.e. crop was having peak vegetative growth in the month of Jan. and reached maturity in the month of Feb.
Conclusion NDVI of multi-date satellite data may be used to separate different crops / crop growth stages.
Thank You !
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