Segmentation and Classification of Land use Land ...

ISSN : 0976-8491 (Online) | ISSN : 2229-4333 (Print)

IJCST Vol. 3, Issue 4, Oct - Dec 2012

Segmentation and Classification of Land use Land Cover Change Detection Using Remotely Sensed Data for Coimbatore District, India 1 1

Dr. K. Thanushkodi, 2Y. Baby Kalpana, 3M. Sharrath

Akshaya College of Engg & Tech., Coimbatore, Tamilnadu, India Dept. of CSE, PPG Inst. of Tech., Coimbatore, Tamilnadu, India

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Abstract Land Use is clearly constrained by environmental factors like soil characteristics, climatic conditions, water sources and vegetation. Changes in Land Use and Land Cover is a dynamic process taking place on the earth surface, and the spatial distribution of the changes that have taken place over a period of time and space is of immense importance in many natural studies. Whether regional or local in scope, remote sensing offers a means of acquiring and presenting land cover data in timely manner. The environmental factors reflect the importance of land as a key and finite resource for most human activities including agriculture, industry, forestry, energy production, settlement, recreation and water sources and storage. Often improper land use is causing various forms of environmental humiliation. For sustainable utilization of the land ecosystems, it is essential to know the natural characteristics, extent and location, its quality, productivity, suitability and limitations of various land uses. Land use/Land cover change has become an important component in current strategies for managing natural resources and monitoring environmental changes. The advancement in the concept of vegetation of the spread and health of the world’s forest, grassland and agricultural resources has become an important priority. Viewing the earth from space is now crucial to the understanding of the influence of man’s activities on his natural resource base over time. Over past years, data from Earth sensing satellites (digital imagery) has become vital in mapping the Earth’s features and infrastructures, managing natural resources and studying environmental change.

new things which are not possible in visible band [3]. Remote sensing is an interesting and exploratory science , as it provides images of areas in a fast and cost efficient manner , and attempts to demonstrate the “ what is happening right now” in a study area. Satellite and digital imagery acquired recently, provide more overall detail to assist the researcher in the classification process. Literature reviews and map interpretation are methods that can also be used for interpretation processes. The remote sensing methodologies can be used to access hard to reach areas for fieldwork and provides a more detailed, permanent and objective survey that offers a different perspective. A variety of change detection methods have been developed now days. Some of the most common methods are (i). Image deferencing (2). Principal component analysis, (3). Post-classification comparison, (4). Change vector analysis (5). Thematic change analysis. Pixels within areas assumed to be automatically homogeneous are analyzed independently. These new sources of high spatial resolution image will increase the amount of information attainable on land cover [2]. For efficient planning and management, the classified data in a timely manner, in order to get the classified data of the ground; satellites are the best resources to provide the data in a timely manner. The LANDSAT 7 completes a full orbit of the earth in about 99 minutes, allowing the satellite to achieve 14 orbits per day. The satellite makes a complete coverage of the earth every 16 days.

Keywords Digital Imagery, Remote Sensing, Forests, Climatic Conditions, LULC Classification, Environmental Factor

A. General Information Area: 7469-sq-km Population: 3,472,578 (2011 Census) Latitude N: 10° 10’ and 11° 30’ Longitude: E 76° 40’ and 77° 30’ Altitude: 43.2 m Clothing: Light Cottons Language Spoken: Tamil, English, Telugu and Malayalam Climate: Tropical Temperature Range (deg C): Summer: Max 39.4ºC, Min 23.3ºC Winter: Max 32.8ºC, Min 20.7ºC Rainfall: Normal : 327mm(NE monsoon) 192.9mm (SW monsoon) Actual : 205.4mm(NE) and 90.1mm (SW).

I. Introduction Satellite and digital imagery play an important role in remote sensing ; providing information about the land suited. Remote sensing systems offer four basic components to measure and record data about an area from a distance. These components include the energy source, the transmission path, the target and the satellite sensor. Here the LANDSAT imagery is used. Remote sensing provides impartant coverage, mapping and classification of landcover features such as vegetation and agriculture, soil (bare soil) , water and forests. Topographic data and a digital elevation model also increase the classification accuracies. Correlations can then be drawn between topographic features in order to show the relationships that occur between forest, vegetation and soils.This provides important information for land classification and land-use management. Significance is that the data can be acquired by our eyes and the energy can be analyzed. But satellites are capable of collecting data beyond the visible band also. This will help us to analyze the

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II. Study Area

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Fig. 1: Study Area of Coimbatore District III. Methodology A digital image is defined as a matrix of digital numbers. Each digital number is the output of the process of analog to digital conversion. The image display subsystem carries out the conversion operation that of taking a digital quantity of an individual pixel value from an image. The pixel value or digital number shows the radiometric resolution of remotely sensing data. The process includes image acquisition, image enhancement, segmentation, Clustering and classification, data modeling. A. Image Acquisition The use of computer – assisted analysis techniques permits the spectral patterns in remote sensing data to be fully examined. It also permits the data analysis process to be largely automated, providing cost advantages over visual interpretation techniques. Satellite remote sensing data in general and digital data in particular have been used as basic for the inventory and mapping of natural resources of the earth surface like agriculture , soils forestry, and geology. The final product in most of the applications (classified outputs) is likely to complement or supplement the map. Numerous electromagnetic and some ultra sonic sensing devices frequently are arranged in an array format. CCD sensors are used in digital cameras and other light sensing instruments [8]. But here in this paper the LANDSAT 7,4,2 imagery is used for the data analysis. 1. Preprocessing Remotely sensed raw data, received from imaging sensor mounted on satellite platforms generally contain flaws and deficiencies. The correction of deficiencies and removal flaws present in the data through some methods are termed as pre processing methods. In other words, to correct distorted or degraded image data to create a more faithful representation of the original scene, image rectification and restoration process is necked which is always called as preprocessing. This correction model involves the initial processing of raw data to correct geometric distortions, to calibrate the data radio metrically and to eliminate the noise present in the data. All pre processing methods are considered under 3 heads, namely, (i) geometric correction methods, (ii) radiometric correction methods. (iii) atmospheric correction methods.


B. Edge Detection An edge is a set of connected pixels that his on the boundary between two regions, Edge detection is performed on the image by the construction of edge detection operators like sobel edge detection, laplacian edge operator etc. For a continuous image f(x,y), where x and y are the row and column coordinates respectively, consider 2D derivatives δyf(x,y) and δxf(x,y) . Two functions can be expressed : Gradient magnitude | Δf(x,y) | = √ ( ∂x f(x,y))2 + ( ∂y f(x,y) )2 Gradient orientation ∟Δf(x,y) = ArcTan (∂y f(x,y)/∂x f(x,y) ) Local maxima of the gradient magnitude identify edges in f(x,y). The first derivative achieves a maximum and the second derivative is zero. For this reason, an alternative edge detection strategy is to locate zeros of the second derivatives of f(x,y) [2].

Fig. 2: Edge Detection Sample o/p C. Image Segmentation Technique Image segmentation is the partition and pick-up of the homogeneous regions of image. In the results of segmentation, the consistency of gray the smoothing of boundary and the connectivity are fulfilled. The classical method of segmentation is the spatial cleaning based on the measurement space [2]. Image segmentation is crucial processing procedure for the classifications and feature extraction of high resolution remote sensing image [5]. The segmentation result is able to sway the effect of subsequent processing. The edge-based segmentation is taken into account which is namely grounded on discontinuity of gray-level in imagery. The image is segmented by the edge of the different homogenous areas [4]. Adopting this method, the accuracy of edge positioning is high whereas the consecutive edge composed of a serial of unique pixels cannot produce, so a sequent process including bulky the detected edge points should he requisite. Algorithm • 3 x 3 mask and to find the marker values, the original intensity values have been set. These values can be compared with neighbor pixel intensity. Due to the discontinuity in pixels, delete the zero marker values (or pixels) to obtain the image regions. • Calculate mean gray values as,

where is the mean intensity value i is the pixel region Ni is the pixels in original image. The original intensity values can be obtained from the original input image. • The minima depth depends on the minimum-pseudo points (i.e., approximate points) and also it considers the waterbasin scale. Where the water basins may generate small regions International Journal of Computer Science And Technology 815



including the little object information. So the generation of meaningless regions can be avoided. The basin scale can be compared with threshold by,

‘Identify’ is an eliminating local minima process. The water basin scale is smaller than the basin scale threshold Bscale. D. Clustering Technique The clustering technique attempts to access the relationships among patterns of the LANDSAT data set by organizing the patterns into groups or clusters such that patterns within a cluster are more similar to each other than patterns belonging to different clusters. That is , clustering refers to the classification of objects into groups according to certain properties of these objects. In a clustering technique, an attempt is made to extract a feature vector from local areas in the images as fig 1,2,3,4. A standard procedure for clustering is to assign each pixel to the class of the nearest cluster mean. There are numerous clustering algorithms that can be used to determine the natural spectral groupings present in a data set. One common form of clustering, called the “K-means” approach accepts from the analyst the number of clusters to be located in the data [6]. The algorithms then arbitrarily “seeds” or locates that number of clusters centers in the multidimensional measurement space [8]. Each pixel in the image is then assigned to the cluster whose arbitrary mean vector is closet. Algorithm • Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids. • Assign each object to the group that has the closest centroid. • When all objects have been assigned, recalculate the positions of the K centroids. • Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated. E. Unsupervised Classification The family of classifiers involves algorithms that examine etc unknown pixels in an image and aggregate them into a number of classes based on the natural groupings or clusters present in the image values. The basic premise is that values within a given cover type should be close together in the measurement space; whereas data in different classes should be comparatively well separated. The classification algorithm is designed to automatically said dense regions within the n-dimensional hyper spectral data cloud [7]. The algorithm is based on the well-known observation that spectra of large, distinct land covers tend to cluster around a mean spectrum. This is the basis for unsupervised classification parcel on cluster analysis [2]. The pixel density around the mean spectrum depends on the spectral variability of the land cover and the areas extent of the land cover.

obtained using ERDAS) Output: Land area used and coverage images and calculated area. Set M value (mixture density distribution) Estimate D Compute value for E using (9) Compute D and new E values Initialize the iterations value i For n images do the following; Read the image where image belongs to the segmented image database For classification Assign and read colors from images Start iteration Identify image boundaries Iterate all colors in image Calculate area; Display area; Compare with previous value and obtain the comparison chart Repeat the steps from 1 to 6.3 obtain maximum E value. IV. Screen Shots and Results

Fig. 3

Fig. 3 & 4: Different Images of Coimbatore District

Algorithm Input : Segmented image The input format shall be *.sig (the signature image

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The remote sensing data have been analyzed to fixed the land cover classification of our city, and to know how the use of land changes according to time and also performed the temporal analysis to analyze [3] all these things, the unsupervised classification method is used. This is very fast and useful analysis method. It is widely used for the crops [10] classification in the world and this classification method is used for land cover and land use because vegetation components are important in the images [9]. The basic axis is also to preserve the greenery of the city for the healthy environment. B. Findings 1. Found that due to the growth of industries , the financial growth will be high. Besides, the pollution rate is also high which results the new diseases. 2. Found that due to the deforestation, the rain fall rate per year is gradually reduced. 3. The monsoon is changed. 4. Due to the increase in population, the residential area is increased, which causes the demolish of fields.

Fig. 4:

VI. Acknowledgement The authors are grateful to the Director , IRS, Anna university,Chennai who helped in this excellent work. The authors are thankful to Anna university Research centre delegates who helped them in all this work since 2007.

Fig. 4 & 5: Classified Images of Coimbatore District

Fig. 6: Segmented Images of Coimbatore District V. Conclusion A. Sample Data Table 1: LAND COVER Water Agriculture Residential area Grassland Bare Soil Forest Industries Total w w w. i j c s t. c o m

HECTARES 86839 74707 16269 100000 160000 15880 100000 553695

References [1] Y. Babykalpana Supervised/Unsupervised Classification of LULC Using Remotely Sensed data, Paper-ID : iCIRET0106, CiiT Internationals Journals-iCIRET-2010 [2] Y. Babykalpana, Dr. K.ThanushKodi, Classification of LULC change detection using Remotely sensed data Coimbatore city, India, Journal of Computing, Vol. 2, Issue 5, May 2010. [3] Jifen Liu, Maoting Gao,“ An Unsupervised Classification Scheme Using PDDP method for network Intrusion Detection”, IEEE transactions 2008, Vol. 8, pp. 658-662. [4] John Cipar, R.L.Wood, T.Cooley,"Testing an Automated Unsupervised Classification Algorithm with Diverse Land covers”, IEEE Transactions 2007, Vol. 1, pp. 2589-2592. [5] LI.Jia-cun, QIAN Shao-meng, CHEN Xue,"Object Oriented Method of Land Cover Change Detection Approach Using High Spatial Resolution Remote Sensing Data”, Vol. 3, IEEE transactions 2003, pp. 3005-3007. [6] S.K.Srivastava,H.K.Singh, A.K.Sinha,“Agriculture land estimation from Satellite Images Using Hybrid Intelligence: A case study on Meerut City”, IJCSKE, Vol. 3, No. 1, Serials publications, Jan-June’09. [7] Thomas M.Liiesand, R.W.Kiefer,“ Remote sensing and Image Interpretation”, 4th edition, John Wiley & sons , Inc., pp. 473-488, 532-558. [8] Yokoyama, Kurosu T., SHI., Fujita, M.,"Land use classification with textural analysis and the aggregation technique using multi-temporal JERS-1 L -band SAR images. International Journal of Remote Sensing, 22, No. 4, pp. 595 -613, 2001. [9] [Online] Available: http://www.gisdevelopment.net/tutorials/ software/index.htm [10] [Online] Available: http://www.csc.noaa.gov/crs/lca/faq_ gen.html#WIRS [11] [Online] Available: http://www.eicinformation.org/data/ requestform.asp?id=0 International Journal of Computer Science And Technology 817



[12] [Online] Available: http://books.google.co.in/books?id=JeD Gn6Wmf1kC&pg=PA376&lpg=PA376&dq=clustering+tec hniques+in+dip [13] [Online] Available: http://gislounge.com/satellite-imagery/

Dr. K.Thanushkodi, is the Director of Akshaya College of Engineering & Technology, Coimbatore . He worked as the Principal, Government college of Engineering, Bargur and as a Principal of CIET, Coimbatore. He has got 30 ½ yrs of teaching experience in Government Engineering Colleges Has published 45 papers in International Journal and Conferences. Guided 1 Ph.D and 1 MS (by Research), Guiding 15 Research Scholars for Ph.D Degree in the areas of Power System Engineering, Power Electronics, Computer Networking & Virtual Instrumentation and One Research Scholar for MS (Research). Principal incharge and Dean, Government College of Engineering, Bargur Served as Senate Member, Periyar University, Salem. Served as member, Research Board, Anna University, Chennai. Served as Member, Academic Council, Anna University, Chennai Serving as Member Board of Studies in Electrical Engineering, Anna University, Chennai. Serving as Member, Board of Studies in Electrical and Electronics & Electronics and Communication Engineering, Amritha Viswa Vidya Peetham, Deemed University, Coimbatore. Serving as Governing Council Member SACS MAVMM Engineering College, Madurai. Mrs.Y.Baby Kalpana is a research scholar. She is pursuing in Anna university of Technology, Coimbatore, under the guidance of Dr. K.Thanushkodi. She is currently working as an Assistant Professor in the Department of computer Science & Engineering of PPG Institute of Technology,Coimbatore. She has published more than 10 papers in this work in 10 different national and international journals. She has guided more than 15 M.E projects amd 30 B.E projects. She is grateful to the Chairman Dr.L.P.Thangavelu , Correspondant Mrs.Shanthi Thangavelu and Principal for their continuous encouragement in pursuing this research work. Mr. M.Sharrath has obtained his Bachelor Degree and Masters Degree in Computer Science and Engineering from Anna University, Chennai. Currently he is an Assistant Professor at PPG Institute of Technology, Coimbatore

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