Dr.N.Chandrasekar Professor and Head Centre for GeoTechnology Manonmaniam Sundaranar University Tirunelveli

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1 Classification and Segmentation of Coastal Landforms in the South Tamilnadu Coast Dr.N.Chandrasekar Professor and Head Centre for GeoTechnology Manonmaniam Sundaranar University Tirunelveli

2 Sky Forest Wasteland Classify land use pattern Settlements Vegetation Foreground, Background separation Mineral Identification Heavy minerals

3 OBJECTIVE To select a statistical and texture features from the IRS1D- Liss III imagery From ground truth information, collect the training samples Classify Satellite imageries with various Supervised, Unsupervised techniques and determine the best classification method. Extract the information landforms and Settlements. from classified Coastal

4 STUDY AREA

5 LOCATION The present study area between Kallar and Vembar lies in Tamilnadu with in the latitudes of 8 55 to 9 5 N and longitudes of to E. The stretch extents over a length of about 24km. It is bounded by Gulf of Mannar in the east, Surangudi in the west, Vembar river in the north and Kallar river in the south. This area is enriched with various placer mineral deposits

6 Geomorphology map of Southern Tamilnadu

7 Geomorphology map of Vembar-Kallar

8 CLASSIFICATION Classification is the act of assigning a class label to an object, a physical process or an event. Class assignment based on measurements that are obtained from that object. Measurements are values of some attributes (features) of the object, e.g. shape, length, texture, color etc.

9 PROCEDURE FOR CLASSIFICATION Preprocessing Segmentation Feature extraction Training samples Classification Post-Classification

10 Preprocessing Segmentation Feature extraction Training samples Classification Post-Classification

11 PRE-PROCESSING To simplify subsequent operations without loosing relevant information. Image Details: IRS-1D-LISS III, 1:25000 scale (June 2002), Coastal area between Kallar and Vembar Original Image Preprocessed Image

12 Preprocessing Segmentation Feature extraction Training samples Classification Post-Classification

13 IMAGE SEGMENTATION A common definition for segmentation is Partitioning a digital image into multiple segments in order to find the objects within the image. Segmentation can recognize homogeneous regions within an image as distinct and belonging to different objects. Segmentation is used to identify the object or measurement of various targets in an satellite image. Major application areas are geological exploration, remote sensing, robot navigation, medical image analysis, and military applications. This presentation focused two types of segmentation application 1. Object identification (Feature selection) 2. Post classification (Class separation)

14 Object identification Segmentation is used to digitally identify and classify pixels in the data. K-means algorithm is a cluster based technique used here for segmentation. K-means algorithm K-means is a unsupervised algorithm used here for image segmentation. This algorithm is composed three steps: 1. Place K points into the space represented by the objects that are being clustered. 2. Assign each object to the group that has the closest centroid. When all objects have been assigned, recalculate the positions of the K centroids. 3. Repeat above until the centroids no longer move.

15 K-MEANS SEGMENTATION RESULTS Pre-processed Image K-means Segmentation

16 Preprocessing Segmentation Feature extraction Training samples Classification Post-Classification

17 FEATURE EXTRACTION Features are patterns or unique structures which is extracted from the object. The number of features is generally chosen to be fewer than the total necessary to describe the complete target of interest. Feature selection Process 1. Spatial features a) Ground truth ROI s 2. Statistical features a) Data range b) Entropy c) Mean d) Skewness e) Variance 3. Texture features a) Homogeneity b) Dissimilarity c) Correlation d) Contrast e) Secondmoment

18 STATISTICAL AND TEXTURE FEATURES Band 1 Data range Entropy Variance Mean Skewness Variance Contrast Homogeneity Dissimilarity Second moment Contrast Homogeneity Dissimilarity Second moment Variance Contrast Homogeneity Dissimilarity Band 2 Data range Entropy Variance Mean Skewness Variance Band 3 Data range Entropy Variance Mean Skewness Second moment

19 Preprocessing Segmentation Feature extraction Training samples Classification Post-Classification

20 TRAINING SAMPLES A set of samples usually called as Training set. The samples are provided with a label carrying the information of the true class of the corresponding object. The density can be estimated from the samples by kernel methods. Here, three kernel methods considered for further analysis. Linear Polynomial Radial Basis Function Cross validation accuracy used here to check the performance of Training samples.

21 TRAINING RESULT Ground truth ROI ROI+ ST parameters

22 Preprocessing Segmentation Feature extraction Training samples Classification Post-Classification

23 CLASSIFICATION METHODS AND TECHNIQUES Two types of classification methods: Supervised Classification Unsupervised Classification Supervised Classification Parallelepiped Minimum Distance Mahalanobis Distance Maximum likelihood Binary encoding Support vector machine Unsupervised Classification 1. IsoData 2. K-means

24 SUPERVISED CLASSIFICATION RESULTS Minimum distance Parallelepiped 4 bands ST features 4 bands Maximum likelihood 4 bands ST features ST features Binary encoding 4 bands ST features

25 Mahalanobis distance 4 bands ST features Support Vector Machine 4 bands ST features UNSUPERVISED CLASSIFICATION RESULTS IsoData (Accuracy: K-means (Accuracy: %)

26 OVERALL ACCURACY (4 bands) Kappa Co-efficient Parelleped (PP) Minimum Distance (MinD) Maximum Likelihood (MaxL) Binary encoding (Bin) Mahalonobis (Mah) Support Vector Machine (SVM)

27 OVERALL RESULTS (ST features) Kappa Co-efficient Parelleped (PP) Minimum Distance (MinD) Maximum Likelihood (MaxL) Binary encoding (Bin) Mahalonobis (Mah) Support Vector Machine (SVM)

28 Preprocessing Segmentation Feature extraction Training samples Classification Post-Classification

29 POST CLASSIFICATION Post Classification enclosed following processes: 1. Class statistics 2. Clump classes 3. Sieve classes 4. Combine classes 5. Overlay Classes 6. Class segmentation 7. Buffer zone 8. Vectorization Thresholding Segmentation Comparing each pixel based on Threshold value in order to separate the class. Each class contain common attribute values. Class separation has to be done by assigning threshold value.

30 CLASS SEGMENTATION RESULTS Classified image Saltpan Creeks Land with scrub Land settlements Mangrove Mud with mangroves River Agriculture Beach ridges Tank Gulf of mannar

31 DISCUSSION From the classification result it is found that Support Vector machine performed well. SVM based classified image considered for Coastal land form studies. Our study area is characterized by a variety of complex environments like beaches, saltpans, river, tanks, dunes and marshes, mangroves, settlements etc. Saltpan From the classified image, extensive area is covered of salt pan near Veppalodai, for about 10.1Km². The dry salt pan appears to be bright white tone.

32 DISCUSSION Tanks and Swales The prominent swale system is found in the coastal plain between Periasamypuram to Vembar river mouth. The swale is almost parallel to the coastline; their width is about 1.5Km. Creek Beach is characterized by numerous creeks. These creeks are responsible for water logging of vast area throughout the year. Mudflat The mudflat areas are seen near Kallar, Tulukankulam, Vaippar and Vembar.

33 DISCUSSION Mangroves and Vegetation From the classified image, Mangrove varieties like Suaeda Monoinca, Suaeda Maritime and Salicornia brachiata are commonly found along the study area. Beach ridges Beach ridges between Kulathur and Vaippar river has covered by Teri sands and plantation vegetation. Beach ridges between Melmandai and east of Surankudi has dune materials like sand gravel and shingle. Rivers the most important river is Vaippar and it is seen in the classified image.

34 R&D Activities in Centre for GeoTechnology Remote sensing and GIS applications on Beach placer minerals evaluation along the coast between Kallar and Vembar - DOD Project. Exploration of Beach placer in Kallar and Vaippar DOD Project. Impact of beach placer mining along the coast of Tamilnadu DST Project. Corel reef monitoring through Remote sensing and GIS in Gulf of Mannar - NATP, ICAR Project. An operation as Marine GIS Expert system for non living resource mapping DST Project.

35 Thank you

Abstract: About the Author:

Abstract: About the Author: REMOTE SENSING AND GIS IN LAND USE PLANNING Sathees kumar P 1, Nisha Radhakrishnan 2 1 1 Ph.D Research Scholar, Department of Civil Engineering, National Institute of Technology, Tiruchirappalli- 620015,