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             \author[1]{Mittu  Mittal}

             \author[2]{Gagandeep  Kaur}

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\date{\small \em Received: 6 December 2012 Accepted: 3 January 2013 Published: 15 January 2013}

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\begin{abstract}
        


Now a day?s Remote Sensing is a mature research area. Remote sensing is defined as a technique for acquiring the information about an object without making physical contact with that image via remote sensors. But the major problem of remotely sensed images is mixed pixel which always degrades the image quality. Mixed pixels are usually the biggest reason for degrading the success in image classification and object recognition. Another major problem is the decomposition of mixed pixels precisely and effectively. Remote sensing data is widely used for the classification of types of features such as vegetation, water body etc but the problem occurs in tagging appropriate class to mixed pixels. In this paper we attempted to present an approach for resolving the mixed pixels by using optimization algorithm i.e. Biogeography based optimization. The main idea is to tag the mixed pixel to a particular class by finding the best suitable class for it using the BBO parameters i.e. Migration and Mutation.

\end{abstract}


\keywords{remote sensing, mixed pixel, biogeography based optimization, migration, mutation, evolutionary algorithms.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction to Remote Sensing}]{Introduction to Remote Sensing}\par
emote sensing is the practice of deriving information about the earth's land and water surfaces using images acquired from an overhead perspective, using electromagnetic radiation in one or more regions of the electromagnetic spectrum, reflected or emitted from the earth's surface. This definition certainly does not cover all areas (e.g. meteorological or terrestrial remote sensing), it does serve well as a description of remote sensing. Remote sensing makes use of electromagnetic radiation. The strongest and best-known source of electromagnetic radiation is our sun, which emits radiation over the entire electromagnetic spectrum see fig \hyperref[b0]{1}. Besides this natural source of illumination, which is used for passive remote sensing, it is also possible to use an artificial source of electromagnetic radiation, in which case we speak of active remote sensing.\par
Author ? : Mtech (C.S.E) Department of CSE, RIMT Institutions, Mandi Gobindgarh, Punjab, India. E-mail : Mittu231188@yahoo.co.in Author ? : (A.P in C.S.E) Department of CSE, RIMT Institutions, Mandi Gobindgarh, Punjab, India. E-mail : gaganarora9@gmail.com Remote sensing map takes pixel as a measurement unit in the ground detection. Since the limited precision of satellite remote sensing detection, sometimes there are several kinds of ground objects in the range of one pixel, which is named mixed pixel. On the contrary, the pixel that consists of one kind of ground object is described as typical pixel. Generally the spectrum features of different ground objects are different. The spectrum of mixed pixels is a mixture of poly-type spectrum of ground objects according to some proportion. The existence of mixed pixel is one of the main factors that affect classifying precision in image recognition. Especially it affects the classification and recognition of linear ground objects and petty ground objects. A lot of mixed pixels are bound to appear at the edge of ground objects. The key to resolve the problem is to find out the Proportion of different objects.\par
There are various methods for the decomposition of mixed pixel as follows: 1. Linear discriminate analysis 2. Probabilistic, geometric-optical 3. Stochastic geometric 4. Fuzzy 5. Artificial Neural Network models 6. Evolutionary algorithms All of the above mentioned methods are traditional.\par
But evolutionary algorithms are new approach for resolving mixed pixel problem of remote sensing images.  
\section[{II. Biogeography Based Optimization}]{II. Biogeography Based Optimization}\par
Biogeography is the study of the distribution of biodiversity spatially and temporally. Over real ecological changes, it is also tied to the concepts of species. Immigration is the introduction of new people into a habitat or population. It is a biological concept and is important in population ecology. A habitat (which is Latin for "it inhabits") is an ecological or environmental area that is inhabited by a particular species of animal, plant or other type of organism. It is the natural environment in which an organism lives, or the physical environment that surrounds (influences and is utilized by) a species population. Biogeography unfolds the geographical distribution of biological organisms. Their interest was primarily focused on the distribution of species among neighboring islands. The application of biogeography to engineering is similar to what has occurred in the past few decades with genetic algorithms (GAs), neural networks, fuzzy logic, particle swarm optimization (PSO), and other areas of computer intelligence. The term "island" here is used descriptively rather than literally. That is, an island is any habitat that is geographically isolated from other habitats. We therefore use the more generic term "habitat" in this paper (rather than "island"). Geographical areas that are well suited as residences for biological species are said to have a high habitat suitability index (HSI). Features that correlate with HIS include such factors as rainfall, diversity of vegetation, diversity of topographic features, land area, and temperature. The variables that characterize habitability are called suitability index Variables (SIVs). SIVs can be considered the independent variables of the habitat, and HSI can be considered the dependent variable. Habitats with a high HSI have many species that emigrate to nearby habitats, simply by virtue of the large number of species that they host. We call this approach to problem solving biogeography-based optimization (BBO).  In many cases this spectral mixing can make it very difficult for the image analyst to identify the different sub-pixel fractional components that serve to make up the landscape under observation. Under these circumstances the analyst may wish to employ a finer resolution data set, in order that a greater number of 'pure' pixels may be recorded. Even with very fine resolution, however, there is still the issue of edge pixels, where pixels can show the boundaries between different land surface properties. Land surface features do not follow the arbitrary confines of the pixel and therefore even very fine spatial resolution data of the order of a few meters' will still experience some degree of confusion and spectral missing.\par
As you can imagine, mixed pixels can cause great difficulties in the stages of image analysis and interpretation. One of the common tasks in this process is image classification. Classification is widely used as it allows users to easily discriminate information from images presented as a series of categories (classes) rather than raw digital number (DN) values. Images are classified on the basis of their spectral properties. Each pixel of remote sensing image contains the information from multifarious ground objects due to the difference from the resolution of remote sensing image, called Mixed pixel.   ? Mixing due to gradual transition observed between continuous thematic classes.\par
To resolve these problems we proposed a new methodology using BBO algorithm which is described in next section. 
\section[{III.}]{III.} 
\section[{Methodology}]{Methodology}\par
INPUT : Data set of pure and mixed pixels ? Read an image and identify the data set of pure and mixed pixels. 
\section[{?}]{?}\par
Clustering is done on the basis of color intensity, texture etc. 
\section[{?}]{?}\par
Initially choose a set of pure pixels and calculate the best pixel value using G.A.  
\section[{Distinctive Features of BBO}]{Distinctive Features of BBO}\par
? First, Biogeography-Based Optimization is a type of evolutionary algorithm. As its name implies, Biogeography is the study of the migration, speciation, and extinction of species. This clearly distinguishes it from reproductive strategies such as GAs and evolutionary strategies. ? BBO also clearly differs from ACO, because ACO generates a new set of solutions with each iteration. BBO, on the other hand, maintains its set of solutions from one iteration to the next, relying on migration to probabilistically adapt those solutions. ? In BBO the original population is not discarded after each generation. It is rather modified by migration.\par
? Another distinctive feature is that, for each generation, BBO uses the fitness of each solution to determine its immigration and emigration rate. ? BBO is easier to implement and there are fewer parameters to adjust. ? BBO has a more effective memory capability than GA.\par
V. 
\section[{Conclusion \& Future Scope}]{Conclusion \& Future Scope}\par
Mixed pixel resolution is a big problem in any remotely sensed imaging. Thus our method can resolve greater number of mixed pixels problem in effective way and also helpful in providing great accuracy in output images. Future work could focus on combining BBO with other EAs like PSO, ABC etc to resolve the problem of mixed pixel resolution. \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Figure 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{2}\includegraphics[]{image-4.png}
\caption{\label{fig_3}Figure 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-5.png}
\caption{\label{fig_4}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3}\includegraphics[]{image-6.png}
\caption{\label{fig_5}Figure 3 F?}\end{figure}
    			\footnote{© 2013 Global Journals Inc. (US)} 			\footnote{© 2013 Global Journals Inc. (US) Year} 			\footnote{Mixed Pixel Resolution by Evolutionary Algorithm: A Survey 2 © 2013 Global Journals Inc. (US) Year} 		 		\backmatter   			 
\subsection[{Acknowledgement}]{Acknowledgement}\par
This study was supported by the Department of computer science \& Engineering of RIMT Institutes Near Floating Restaurant, Sirhind Side, Mandi Gobindgarh-147301, and Punjab, India. 			  			  				\begin{bibitemlist}{1}
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\end{bibitemlist}
 			 		 	 
\end{document}
