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 !

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