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\title{Crop Coverage data Classification using Support Vector Machine}
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             \author[1]{Tarun  Rao}

             \affil[1]{  Dayananda Sagar College of Engineering}

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\date{\small \em Received: 10 December 2015 Accepted: 31 December 2015 Published: 15 January 2016}

\maketitle


\begin{abstract}
        


A statistical tool which can be used in various applications ranging from medical science to agricultural science is support vector machines. The proposed methodology used is support vector machine and it isused to classify a raster map. The dataset used herein is of Gujarat state agriculture map. The proposed approach is used to classify raster map into groups based on crop coverage of various crops. One group represents rice crop coverageand the othermillets crop coverage and yet another that of cotton crop coverage.Various statistical parameters are used to measure the efficacy of the proposed methodology employed.

\end{abstract}


\keywords{Mining, SVM, supervised classification}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
rop mapping is widely used in agriculture andremote sensing science. Crop mapping using classification methodologies serves various applications in agricultural sciencelike gauging water and soil demand etc.. For such applications information on the spatial distribution of classification error is of particular interest  {\ref [1]}.Recent progresses in Information Technology systems, lead to dynamic communication among people of every profession. Information technology systems have changed the way people meet and communicate. There is an increasing tendency of professionals and experts in the agriculture sector to communication best practices in the field of agriculture via the medium of internet. Farmers who use the medium of internet get benefited from the various forums used therein to communicate advanced crop yield technologies. Crop mapping can also facilitate the farmers in planning their crop management in advance and they do not see internet and modern technologies has a hurdle  {\ref [2]}.\par
Data is everywhere, abundant, continuous, increasing and heterogeneous. Extracting meaningful information from that data is useful but very difficult: rich data but poor information is a common phenomenon in the world. Data mining (DM) refer to extracting or mining useful knowledge from large amounts of data. One of the various phases of data mining is classification.\par
Classification is the process in which available data items are categorized into two or more categories depending on the various criterions. Methodologies in which the class label is known apriori is called supervised classification and those in which class labels are not known apriori are called unsupervised classificationor clustering  {\ref [3]}. Supervised classification can be further categorized as parametric and nonparametric categories. Based on whether or not the approach is based on probability distribution or density functions  {\ref [4]}.\par
A well-known statistical method that can be used to solve optimization problems is Support Vector machines (SVM). The proposed methodology used here is SVM. The data items can be represented as feature vectors in a hyper plane and a line passing through the hyper plane can be used to categorize the data items into two different categories. The line can be considered a naïve form of SVM  {\ref [6] [7]}. The An advantage of SVM as a classification method is that in has feature extraction method in-built in its architecture. SVM is better compared to other existing classification methodologies like Naïve bayes, Artificial neural network, decision tree based classification etc. depending on previous research[8] \hyperref[b1]{[9]}.\par
SVM which is inherently linear in nature. However by using kernel function it can be extended to non-linear space as well. In either of the approaches SVM takes a lot of time to classify the data items. SVM approach is used to solve a multi-class classification problem in this research work Its finds a suitable line which is far off from all equidistant points in the hyper plane \hyperref[b2]{[10]}\hyperref[b3]{[11]}\hyperref[b4]{[12]}\hyperref[b5]{[13]}\hyperref[b6]{[14]}\hyperref[b7]{[15]}\hyperref[b8]{[16]}.\par
SVM has numerous applicationsas inland analysis \hyperref[b2]{[10]}, species mapping \hyperref[b3]{[11]}, medicine \hyperref[b1]{[9]}, error identification \hyperref[b4]{[12]}, text and speech analysis[5,13], signal analysis[14etc... SVM is used in this research to classify raster TIFF datasets. Subsequent section explains about Literature Survey on SVM. Later Proposed methodology is explained followed by result analysis. The final section deals with conclusion followed by references. 
\section[{II.}]{II.}\par
Literature Survey a) Introduction to SVM SVM isa promising methodology which is used in various applications. It solves both two class and multi-class problems \hyperref[b7]{[15]} \hyperref[b8]{[16]}. Problems in which input data items need to categorized into two categories are called two class problems and the ones in which data items need to be categorized into multiple classes are called multi-class problems \hyperref[b9]{[17]}. The multi-class classification problem can be solved using divide and conquer approach. In this approach the problem can be divided into many two-class problems and in the future the results can be merged to arrive at the final solution to the problem. 
\section[{b) SVM has two major features}]{b) SVM has two major features} 
\section[{Margin maximization:}]{Margin maximization:}\par
The classification boundary functions of SVMs maximize the margins, which leads to maximizing generalization performance \hyperref[b10]{[18]}.\par
SVM can be categorized as linear and nonlinear SVM as in Fig 1  {\ref .} In linear SVM the hyper plane categorized under two different class labels by a line passing through the hyperplane \hyperref[b10]{[18]}[19] \hyperref[b12]{[20]}. 
\section[{Fig .1 : Linear SVM}]{Fig .1 : Linear SVM}\par
The line representing the SVM can be denoted by (1) \hyperref[b13]{[21]}:m?i+ c> = + 1 m?i+ c< = -1\textbf{(1)}\par
Data items can be represented by (2) \hyperref[b14]{[22]}:f(x)= sign(mc+ b)\textbf{(2)}\par
Where sign() represents sign function, m denotes slope and ? happens to be the angle. Sign function is denoted by: sign(c)=?1 if c > 0 0 if c = 0 ?1 if c < 0 (3)\par
Numerous lines might be able to split the planeas different categories but the one that maximizes the distance between itself and the data items in the two categories is known as the support vector as denoted in Figure \hyperref[fig_0]{2}. The above distance cam be denoted as:M= ?? + ?? -?.m |m| = 2 |m| (4) h(m)= 1 2 m t m (5) subject to y i (m? i + b) >=1,?i\par
The solution can be denoted with the help of a Lagrange multiplier ? i as: m=? ?? i ?? i ? i b=y k -m t x k for any x k such that Lagrange multiplier ? k \#0 (6) Classifier representation \hyperref[b8]{[16]}:f(?)=? ? i y i ? i x + b (7)\par
Systematic nonlinear classification via kernel tricks: SVMs effectively handle non-linear classifications using kernel tricks.\par
To improve the efficiency of the solution the input data item space can be mapped to a higher dimensional feature space denoted by \hyperref[b10]{[18]}:K(?i,?j)=f(? i ) t .f(? j ) (8)\par
The above representation is also known as a kernel function and can be denoted as [23]:Linear Kernel function =? i t ? j Polynomial kernel function = (1 + ? i t ? j ) p Gaussian kernel = exp(- |?i??j| 2 2? 2 ) Sigmoid kernel = tanh(? 0 ?i?j+? 1 ) (9) c) Multi-class SVM\par
Multi class SVM can be categorized as oneversus-all, one-versus-one, and k-class SVM's \hyperref[b10]{[18]}.\par
In this approach SVM classifiers are constructed which separate one class from remaining patterns \hyperref[b10]{[18]}.\par
In this approach k different SVM classifiers are constructed for every pair of classes \hyperref[b10]{[18]}.\par
In this approachK binary classifiersare built concurrently \hyperref[b10]{[18]}.\par
iii. Step 3: If a data item is not assigned any of theregions mentioned then add it to set of support vectors V 
\section[{Proposed Methodology}]{Proposed Methodology}\par
Step 4: end loop 
\section[{End}]{End}\par
Finally the built model is validated against the test data set. Herein the test data set under consideration is the crop coverage area that is not covered as part of the selected training data set sample. One of the key steps involved in the classification process is feature extraction as mentioned below:\par
Energy (E): It facilitates in computing homogeneousness in the data set and is denoted by:E = ? ? (p(i, j)) 2 n?1 j=1 m?1 i=1 (9)\par
Contrast(C): Contrast helps identify local data set variation and is denoted as:C = ? ? (i ? j) 2 p(i, j) n?1 j=1 m?1 i=1\textbf{(10)}\par
Inverse difference moment (IDM): Local texture alterations can be located using:IDM = ? ? 1 1+(j?2) 2 p(i, j)\par
n?1 j=1 m?1 i=1 \hyperref[b3]{(11)} Entropy (S): The data set complexity can be computed by: S = ? ? p(i, j)log (p(i, j))\par
n?1 j=1 m?1 i=1 \hyperref[b4]{(12)} Where ? k and mxn are the mean and size of the blockB k Spatial Frequency (SF): Frequency changes in the data set can be computed using:SF = (RF ) 2 + (CF ) 2 ? ? ? ? ? Where RF = ? 1 m×n ? ? [I(i, j) ? I(i, j ? 1)] 2 n j=2 m i=1 and CF = ? 1 m × n ? ?[I(i, j) ? I(i ? 1, j)] 2 n j=2 m i=1\par
Variance (V): Level of focus in a data set can be computed using:V = 1 m×n ? ? (I n j=1 (i, j) ? ?) 2 m i=1 (\textbf{16})\par
Where ? is the mean value of the block image and m × n is the image size Energy of Gradient (EOG): Measure of focus can also be computed using:EOG = ? ? (f i 2 n?1 j=1 + f j 2 ) m?1 i=1\textbf{(17)}\par
Where, f i = f (i+1, j) -f (i, j)f j = f (i, j+1) -f (i, j)\par
IV. 
\section[{Result Analysis a) Environment Setting}]{Result Analysis a) Environment Setting}\par
Agricultural map of Gujarat was used as a dataset to perform the said classification. A region of interest (ROI) was extracted from the map that acted as a training data and it was validated against the complete data segment pertaining to a particular crop in the map. Environment in which the research was undertaken is shown in Table  {\ref 1}[27].  
\section[{b) Result Analysis}]{b) Result Analysis}\par
The ratio of correctly classified and uncorrectly classified data items can be represented using confusion matrix view as mentioned in Table  {\ref 2}. It helps measure the efficacy of the performed classification. Classification results is given in Figure  {\ref 4}.  
\section[{Table. 2 : Confusion Matrix}]{Table. 2 : Confusion Matrix}\par
Confusion matrix in research is mentioned in Table  {\ref 3}. \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Fig. 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}C}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-4.png}
\caption{\label{fig_2}Fig. 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{.} \par 
\begin{longtable}{P{0.22666666666666666\textwidth}P{0.6233333333333333\textwidth}}
\tabcellsep 1 : Environment Setting\\
Item\tabcellsep Capacity\\
CPU\tabcellsep Intel CPU @2 GHz processor\\
Memory/OS\tabcellsep 4GB /WIN 7\\
Applications\tabcellsep Monteverdi\end{longtable} \par
 
\caption{\label{tab_0}Table .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{.} \par 
\begin{longtable}{P{0.5619444444444445\textwidth}P{0.25027777777777777\textwidth}P{0.01888888888888889\textwidth}P{0.01888888888888889\textwidth}}
Year 2016\tabcellsep \tabcellsep \tabcellsep \\
4\tabcellsep \tabcellsep \tabcellsep \\
Volume XVI Issue III Version I\tabcellsep \tabcellsep \tabcellsep \\
( )\tabcellsep \tabcellsep \tabcellsep \\
Global Journal of Computer Science and Technology\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep \multicolumn{3}{l}{3 : Confusion Matrix}\\
Prediction\tabcellsep \multicolumn{3}{l}{Reference Rice Millets Cotton}\\
Rice\tabcellsep 14\tabcellsep 0\tabcellsep 0\\
Millets\tabcellsep 0\tabcellsep 16\tabcellsep 0\\
Cotton\tabcellsep 0\tabcellsep 0\tabcellsep 11\end{longtable} \par
 
\caption{\label{tab_1}Table .}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.24363057324840762\textwidth}P{0.37356687898089175\textwidth}P{0.05955414012738853\textwidth}P{0.17324840764331212\textwidth}}
\tabcellsep Data set type\tabcellsep Accuracy\tabcellsep Kappa Statistics\\
\tabcellsep Raster TIFF\tabcellsep 100\tabcellsep 100\\
\tabcellsep datasets\tabcellsep \\
\tabcellsep \tabcellsep \tabcellsep Year 2016\\
\tabcellsep \tabcellsep \tabcellsep 5\\
\tabcellsep \multicolumn{3}{l}{References Références Referencias}\\
\tabcellsep \tabcellsep \tabcellsep ( )\\
Kappa statistics=Sensitivity + Specificity -1\tabcellsep (19)\tabcellsep \end{longtable} \par
  {\small\itshape [Note: 1and Electronics in Agriculture, Volume 115, July 2015, Pages 171-179, ISSN 0168-1699,http://dx. doi.org/10. 1016/j.compag.2015.05.001. 2. Henrik Skov Midtiby, Björn Åstrand, Ole Jørgensen, Rasmus Nyholm Jørgensen, Upper limit for contextbased crop classification in robotic weeding applications, Biosystems Engineering, Available online 15 February 2016, ISSN 1537-5110, http:// dx.doi.org/10.1016/j.biosystemseng.2016.01.012. 3. M. Pérez-Ortiz, J.M. Peña, P.A. Gutiérrez, J. Torres-Sánchez, C. Hervás-Martínez, F. López-Granados, A semi-supervised system for weed mapping in sunflower crops using unmanned aerial vehicles and a crop row detection method, Applied Soft Computing, Volume 37, December 2015, Pages 533-544, ISSN 1568-4946, http://dx.doi.org/10.1016 / j.asoc.2015.08.027. 4. Sybrand Jacobus Muller, Adriaan van Niekerk, An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level, International Journal of Applied Earth Observation and Geoinformation, Volume 49, July 2016, Pages 138-150, ISSN0303-2434,http://dx.doi. org/10.1016/j.jag 2016.0 2005. 5. Murat Olgun, Ahmet Okan Onarcan, Kemal Özkan, ?ahinI?ik, Okan Sezer, Kurtulu? Özgi?i, Nazife Gözde Ayter, Zekiye Budak Ba?çiftçi, Murat Ardiç, Onur Koyuncu, Wheat grain classification by using dense SIFT features with SVM classifier, Computers and Electronics in Agriculture, Volume 122, March 2016, Pages 185-190, ISSN 0168-1699, http://dx. doi.org/10.1016/j.compag.2016.01.033. 6. P.J. García Nieto, E. García-Gonzalo, J.R. Alonso Fernández, C. Díaz Muñiz, A hybrid PSO optimized C © 2016 Global Journals Inc. (US)]} 
\caption{\label{tab_2}C}\end{figure}
 			\footnote{© 2016 Global Journals Inc. (US)} 			\footnote{© 2016 Global Journals Inc. (US) 1} 		 		\backmatter  			  				\begin{bibitemlist}{1}
\bibitem[-45]{b0}\label{b0} 	 		 \xref{http://dx.doi.org/10.1016/j.cviu.2015.06.003}{10.1016/j.cviu.2015.06.003}.  		 \url{http://dx.doi.org/10.1016/j.cviu.2015.06.003}  		\textit{-45},  				 	 
\bibitem[Sadat Mozafari and Jamzad (2016)]{b12}\label{b12} 	 		\textit{A SVMbased model-transferring method for heterogeneous domain adaptation, Pattern Recognition},  		 			Azadeh Sadat Mozafari 		,  		 			Mansour Jamzad 		.  		 \xref{http://dx.doi.org/10.1016/j.patcog.2016.03.009}{10.1016/j.patcog.2016.03.009}.  		 \url{http://dx.doi.org/10.1016/j.patcog.2016.03.009}  		August 2016. 56 p. .  	 
\bibitem[Yan et al. (2016)]{b11}\label{b11} 	 		‘An enhanced M-ary SVM algorithm for multi-category classification and its application’.  		 			Yi Yan 		,  		 			Wei Kai Zheng 		,  		 			Jianbao 		,  		 			Ran Liu 		.  		 \xref{http://dx.doi.org/10.1016/j.neucom.2015.08.101}{10.1016/j.neucom.2015.08.101}.  		 \url{http://dx.doi.org/10.1016/j.neucom.2015.08.101}  	 	 		\textit{Neurocomputing}  		 0925-2312.  		26 April 2016. 187.  	 	 (Pages 119-125) 
\bibitem[Wahab et al. (2016)]{b9}\label{b9} 	 		‘CEAP: SVM-based intelligent detection model for clustered vehicular ad hoc networks’.  		 			Omar Abdel Wahab 		,  		 			Azzam Mourad 		,  		 			Hadi Otrok 		,  		 			Jamal Bentahar 		.  		 \xref{http://dx.doi.org/10.1016/j.eswa.2015.12.006}{10.1016/j.eswa.2015.12.006}.  		 \url{http://dx.doi.org/10.1016/j.eswa.2015.12.006}  	 	 		\textit{Expert Systems with Applications}  		 0957-4174.  		15 May 2016. 50 p. .  	 
\bibitem[Dileep and Sekhar (2014)]{b5}\label{b5} 	 		‘Class-specific GMM based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines’.  		 			A D Dileep 		,  		 			C Chandra Sekhar 		.  		 \xref{http://dx.doi.org/10.1016/j.specom.2013.09.010}{10.1016/j.specom.2013.09.010}.  		 \url{http://dx.doi.org/10.1016/j.specom.2013.09.010}  	 	 		\textit{Speech Communication}  		 0167-6393.  		February 2014. 57 p. .  	 
\bibitem[Sunil et al. (2015)]{b1}\label{b1} 	 		‘Comparison of classifiers for lip reading with CUAVE and TULIPS database’.  		 			S Sunil 		,  		 			Suprava Morade 		,  		 			Patnaik 		.  		 \xref{http://dx.doi.org/10.1016/j.ijleo.2015.08.192}{10.1016/j.ijleo.2015.08.192}.  		 \url{http://dx.doi.org/10.1016/j.ijleo.2015.08.192}  	 	 		\textit{Optik -International Journal for Light and Electron Optics}  		 0030-4026.  		December 2015. 126.  	 	 (Pages 5753-5761) 
\bibitem[Shao and Lunetta (2012)]{b2}\label{b2} 	 		‘Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points’.  		 			Yang Shao 		,  		 			Ross S Lunetta 		.  		 \xref{http://dx.doi.org/10.1016/j.isprsjprs.2012.04.001}{10.1016/j.isprsjprs.2012.04.001}.  		 \url{http://dx.doi.org/10.1016/j.isprsjprs.2012.04.001}  	 	 		\textit{ISPRS Journal of Photogrammetry and Remote Sensing}  		 0924-2716.  		June 2012. 70 p. .  	 
\bibitem[Rao et al. (2013)]{b6}\label{b6} 	 		‘Drainage water level classification using support vector machines’.  		 			T Rao 		,  		 			N Rajasekhar 		,  		 			T V Rajinikanth 		.  		 \xref{http://dx.doi.org/10.1109/NUiCONE.2013.6780068}{10.1109/NUiCONE.2013.6780068}.  	 	 		\textit{Nirma University International Conference on},  				2013. 28-30 Nov. 2013doi. 6.  	 	 (Engineering (NUiCONE)) 
\bibitem[Leng et al. (2016)]{b14}\label{b14} 	 		\textit{Employing unlabeled data to improve the classification performance of SVM, and its application in audio event classification, Knowledge-Based Systems},  		 			Yan Leng 		,  		 			Chengli Sun 		,  		 			Xinyan Xu 		,  		 			Qi Yuan 		,  		 			Shuning Xing 		,  		 			Honglin Wan 		,  		 			Jingjing Wang 		,  		 			Dengwang Li 		.  		 \xref{http://dx.doi.org/10.1016/j.knosys.2016.01.029}{10.1016/j.knosys.2016.01.029}.  		 \url{http://dx.doi.org/10.1016/j.knosys.2016.01.029}  		15 April 2016. 98 p. .  	 
\bibitem[Tang et al. (2014)]{b4}\label{b4} 	 		\textit{Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine, Renewable Energy},  		 			Baoping Tang 		,  		 			Tao Song 		,  		 			Feng Li 		,  		 			Lei Deng 		.  		 \xref{http://dx.doi.org/10.1016/j.renene.2013.06.025}{10.1016/j.renene.2013.06.025}.  		 \url{http://dx.doi.org/10.1016/j.renene.2013.06.025}  		February 2014. 62.  	 
\bibitem[Zintzaras and Kowald (2010)]{b8}\label{b8} 	 		‘Forest classification trees and forest support vector machines algorithms: Demonstration using microarray data’.  		 			Elias Zintzaras 		,  		 			Axel Kowald 		.  	 	 		\textit{Computers in Biology and Medicine}  		May 2010. 40 p. .  	 
\bibitem[Erfani et al. (2016)]{b10}\label{b10} 	 		\textit{High-Dimensional and Large-Scale Anomaly Detection using a Linear One-Class SVM with Deep Learning, Pattern Recognition},  		 			Sarah M Erfani 		,  		 			Sutharshan Rajasegarar 		,  		 			Shanika Karunasekera 		,  		 			Christopher Leckie 		.  		 \xref{http://dx.doi.org/10.1016/j.patcog.2016.03.028}{10.1016/j.patcog.2016.03.028}.  		 \url{http://dx.doi.org/10.1016/j.patcog.2016.03.028}  		14 April 2016.  	 
\bibitem[Lam Hong Lee et al. ()]{b7}\label{b7} 	 		\textit{Oil and gas pipeline failure prediction system using long range ultrasonic transducers and Euclidean-Support Vector Machines classification approach, Expert Systems with Applications},  		 			Rajprasad Lam Hong Lee 		,  		 			Lai Hung Rajkumar 		,  		 			Chin Lo 		,  		 			Dino Heng Wan 		,  		 			Isa 		.  		 \xref{http://dx.doi.org/10.1016/j.eswa.2012.10.006}{10.1016/j.eswa.2012.10.006}.  		 \url{http://dx.doi.org/10.1016/j.eswa.2012.10.006}  		May 2013. 1925-1934. 40.  	 
\bibitem[Tahir and Khan (2016)]{b13}\label{b13} 	 		‘Protein subcellular localization of fluorescence microscopy images: Employing new statistical and Texton based image features and SVM based ensemble classification’.  		 			Muhammad Tahir 		,  		 			Asifullah Khan 		.  		 \xref{http://dx.doi.org/10.1016/j.ins.2016.01.064}{10.1016/j.ins.2016.01.064}.  		 \url{http://dx.doi.org/10.1016/j.ins.2016.01.064}  	 	 		\textit{Information Sciences}  		 0020-0255.  		1 June 2016. 345.  	 	 (Pages 65-80) 
\bibitem[Lin et al. ()]{b3}\label{b3} 	 		‘Study on Recognition of Bird Species in Minjiang River Estuary Wetland’.  		 			Hongji Lin 		,  		 			Han Lin 		,  		 			Weibin Chen 		.  		 \xref{http://dx.doi.org/10.1016/j.proenv.2011.09.386}{10.1016/j.proenv.2011.09.386}.  		 \url{http://dx.doi.org/10.1016/j.proenv.2011.09.386}  	 	 		\textit{Procedia Environmental Sciences}  		 1878-0296.  		2011. 10 p. .  	 
\end{bibitemlist}
 			 		 	 
\end{document}
