ashoka socpros2012 paper 62

8/12/2019 Ashoka SocProS2012 Paper 62

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MULTI-TEMPORAL SATELLITE IMAGE ANALYSIS

USING GENE EXPRESSION PROGRAMMING

J. Senthilnath , S.N. Omkar , V. Mani , Ashoka Vanjare,P.G. Diwakar

Ashoka Vanjare SocPros 2012 1


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Outline

Motivation Objective Problem formulation Methodology

Study area Results & discussion Conclusions & future work References Acknowledgement Recognitions



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Earth observation sensor namely optical Moderate ResolutionImaging Spectroradiometer (MODIS) image is useful for floodassessment application

The spectral (pixel) classification is carried out using classificationtechniques.

Incorporating spatial features with spectral information results ingood accuracy.

To overcome this problem nature inspired algorithm is used.

Motivation



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Objective

Multi-temporal MODIS satellite image analysis using Artificial NeuralNetwork (ANN) and Gene Expression Programming (GEP) classifiers to assessflooded regions.

Use of image low resolution image about 250 meters square

Spectral features of an image is used to differentiate water and non-waterimage pixels

An automatic extraction of river networks (using imagery before floodingand during flooding)

Evaluating extracted floods results with ground truth data



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Problem formulation

Identification of similar water image pixels which is obtained bypartitioning the image and grouping similar pixels

The given problem is solved by grouping of pixels i.e. by imagesegmentation through artificial neural network and Gene expression

programming methods.



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Methodology

Here we first classify and then segment

Image Classification Two supervised classification methods Artificial Neural Network and GEPare used to classify the similar spectral water features of an image

Training data set are created in order to identify similar pixels.

During classification, some of water image pixels are misclassification as

non-water image pixels so it is resolved by using region-growing andknowledge-based techniques



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Methodology For the given training data set, ANN generates weights where as GEP provides

mathematical expression

Using these weights and mathematical expression all pixels of the image are

extracted and evaluated



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MethodologyImage Segmentation Image segmentation is to eliminate those pixels that are wrongly classified aswater image pixels

By using shape index and density index are used for identifying non linearfeatures like river.

A

P SI

4 )()(1 Y VAR X VAR

A DI

Where;P- represents the perimeter of the region

A -represents the area of the region)( X VAR -represents variance of x -coordinates

)(Y VAR -represents variance of y -coordinates



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Methodology The river image pixels have a high Shape Index and a low Density Index,whereas non-river pixels have a low Shape Index and a high Density Index

So a threshold values are set for both indices for segmenting water image

pixels from non-water pixels



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Study Area


Figure 1: Study area covers the districts - Kurnool,Mahaboobnagar, Bellary, Gulbarga and Raichur

The study are is Krishna river in south India and images cover anarea of 75.55 km 2


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Results and Discussions

Figure 2. (a) MODIS image of Krishna River before flood. (b) Segmented Image usingANN. (c) Segmented Image using GEP

The two classifier are used to extract the course of the river from theMarch 2009 image (i.e. before the flooding)



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Fig. 3. (a) MODIS during flood image with ground truth informationflooded (white dots with black dots) and non-flooded cities (white dots). (b) Segmented Image usingANN (White pts. are identified as flooded cities). (c) Segmented Image using GEP (White pts. are

identified as flooded cities).


Two MODIS images (i.e. before flooding and during flooding) of the same regionare used to classify, segment and validate.

The classifiers were trained using 20 randomly picked samples of two classes(water and non-water) and the whole image was tested using the trainedclassifiers.


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Where; B2 represent the Band 2 of the MODISimage.

5.4)2*2( B M

)2*3.11(3.3 B N

If the value of M and N for a pixel is > 0.5 then this pixel is classified aswater; else non-water pixel

Further misclassified are segmented using region-based segmentationtechnique.

GEP expression tree generated for the training samples using before andduring flooded image



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Results and Discussions For image segmenting, we have used two indices - Shape Index (SI) andDensity Index (DI)

These indices are used to differentiate the water image pixels from non-water pixels

For March 2009 image, we have used a SI threshold value of 2.7 and a DIthreshold value of 0.8

For September 2009 image, we have used SI threshold value of 2.7 anda DI threshold value of 1.2

After segmenting by ANN and GEP, we have verified it with ground truthdata

The performance of GEP is better than ANN in water extraction of both

before and during flood imagesAshoka Vanjare SocPros 2012 14


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Image RMSE of ANN RMSE of GEP

Before flood(March 2009)

0.1260 0.1205

Features ANN GEP

True positive rate 0.83 1

True negative rate 0.94 0.94

False positive rate 0.06 0.06

Accuracy 0.89 0.96

Table 1. RMSE for before flood image obtained by the two classification techniques

Table 2. Evaluating features based on ROC for during flood image


Root Mean Square Error (RMSE) parameters is used for segmentation evaluation

Receiver Operating Characteristic (ROC) parameter is used for extraction evaluation

Based on the RMSE values for before flood image, GEP classification results inless error in extraction than ANN classification

GEP produces less error than ANN



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Figure 2. (a) MODIS during flood image with ground truth informationflooded and unflooded cities. (b) Segmented Image using ANN (White pts. areflooded cities). (c) Segmented Image using GEP (White pts. are flooded cities).

Notrecognizedplace by ANN



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The tasks of river mapping and flood extraction are accomplishedsuccessfully by the procedure of pixel-based spectral information forclassification, and shape information for segmentation.

In the classification stage of extracting water and non-water groups, thegene expression programming classifier proved better than the artificialneural network classifier.

The results of classification using spectral information are improvedthrough region-growing image segmentation (based on spatial feature)using similarity criteria emphasizing shape information.

In future, research work is carried out using high resolution image andradar images

Conclusions and future works



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[1] Brakenridge, R., Anderson, E.: MODIS-based flood detection, mapping and measurement: The potential foroperational hydrological applications. Transboundary Floods: Reducing Risks through Flood Management Springer-Verlag, 1 12, (2006). doi: 10.1007/1-4020-4902-1[2] Khan, S. I., Hong, Y., Wang, J., Yilmaz, K. K., Gourley, J. J., Adler, R. F., Brakenridge, G. R., Policelli, F., Habib, S., Irwin,D. : Satellite Remote Sensing and Hydrologic Modeling for Flood Inundation Mapping in Lake Victoria Basin:Implications for Hydrologic Prediction in Ungauged Basins. IEEE Transactions on Geoscience and Remote Sensing, 49,85 95, (2011), doi: 10.1109/TGRS.2010.2057513[3] Mingjun, S., Daniel, C.: Road Extraction Using SVM and Image Segmentation, Photogrammetric Engineering andRemote Sensing, 70(12), 1365 1371, (2004)

[4] Wang, Z., Zhang, D.: Progressive switching median filter for the removal of impulse noise from highly corruptedimages, IEEE Transactions on Circuits and Systems II, 46, 78 80, (1999) doi: 10.1109/82.749102[5] Haykin, S.: Neural Networks - A Comprehensive Foundation, 2E, Pearson Prentice hall publication, (1994)[6] Ferreira, C.: Gene Expression Programming: a New Adaptive Algorithm for Solving Problems. Complex Systems,13(2), 87-129, (2001)[7] C. S. Arvind, Ashoka Vanjare, S. N. Omkar, J. Senthilnath, V. Mani and P. G. Diwakar.: Multi-temporal Satellite ImageAnalysis Using Unsupervised Techniques", Advances in computing and information technology. Advances in IntelligentSystems and Computing, 177, 757-765, (2012) doi: 10.1007/978-3-642-31552-7_77[8] Fawcett, T.: an introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861 874, (2006), doi:10.1016/j.patrec.2005.10.010.[9] J. Senthilnath, Shivesh Bajpai, S.N. Omkar, P.G. Diwakar, V. Mani: An approach to multi-temporal MODIS imageanalysis using image classification and segmentation, Advances in Space Research, 50(9), 1274 1287, (2012) doi:http://dx.doi.org/10.1016/j.asr.2012.07.003[10] Omkar, S.N., Mani, V., Diwakar, P.G, Senthilnath, J., Ashoka Vanjare: Multi-temporal time series analysis of satelliteimage, Technical Report no: AE/ISTC/SNO/10/238/02, IISc, Department of Aerospace Engineering, Bangalore.


References


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This work is supported by the Space Technology Cell, Indian Institute ofScience, Bangalore and Indian Space Research Organization (ISRO).

We also acknowledge the MODIS mission scientists and associated NASApersonnel for the production of the remote sensing data which is used inthis paper.


Acknowledgement


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Recognitions-Related works


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Thank you


ashoka socpros2012 paper 62

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