Supervised Image Classification Techniques

Supervised Image Classification Techniques Introduction In this chapter, a review of Web-Based GIS Technology and Satellite image classification techniques. Section 2.2 presents a review of Web-Based GIS Technology.in section 2.3 Satellite images classification techniques are reviewed.In section 2.4 presents the related work .section 2.5 presents uses of web based GIS applications in real world. Section 2.6 presents available commercial web GIS sites. Section 2.7 reviews the types of Geospatial Web Services (OGC) 2.3 Image Classification Image classification is a procedure to automatically categorize all pixels in an Image of a terrain into land cover classes. Normally, multispectral data are used to Perform the classification of the spectral pattern present within the data for each pixel is used as the numerical basis for categorization. This concept is dealt under the Broad subject, namely, Pattern Recognition. Spectral pattern recognition refers to the Family of classification procedures that utilizes this pixel-by-pixel spectral information as the basis for automated land cover classification. Spatial pattern recognition involves the categorization of image pixels on the basis of the spatial relationship with pixels surrounding them. Image classification techniques are grouped into two types, namely supervised and unsupervised[1]. The classification process may also include features, Such as, land surface elevation and the soil type that are not derived from the image. Two categories of classification are contain ed different types of techniques can be seen in fig Fig. 1 Flow Chart showing Image Classification[1] 2.3 Basic steps to apply Supervised Classification A supervised classification algorithm requires a training sample for each class, that is, a collection of data points known to have come from the class of interest. The classification is thus based on how close a point to be classified is to each training sample. We shall not attempt to define the word close other than to say that both Geometric and statistical distance measures are used in practical pattern recognition algorithms. The training samples are representative of the known classes of interest to the analyst. Classification methods that relay on use of training patterns are called supervised classification methods[1]. The three basic steps (Fig. 2) involved in a typical supervised classification procedure are as follows: Fig. 2. Basic steps supervised classification [1] (i) Training stage: The analyst identifies representative training areas and develops numerical descriptions of the spectral signatures of each land cover type of interest in the scene. (ii) The classification stag(Decision Rule)e: Each pixel in the image data set IS categorized into the land cover class it most closely resembles. If the pixel is insufficiently similar to any training data set it is usually labeled Unknown. (iii) The output stage: The results may be used in a number of different ways. Three typical forms of output products are thematic maps, tables and digital data files which become input data for GIS. The output of image classification becomes input for GIS for spatial analysis of the terrain. Fig. 2 depicts the flow of operations to be performed during image classification of remotely sensed data of an area which ultimately leads to create database as an input for GIS. Plate 6 shows the land use/ land cover color coded image, which is an output of image 2.3.1 Decision Rule in image classiffication After the signatures are defined, the pixels of the image are sorted into classes based on the signatures by use of a classification decision rule. The decision rule is a mathematical algorithm that, using data contained in the signature, performs the actual sorting of pixels into distinct class values[2]. There are a number of powerful supervised classifiers based on the statistics, which are commonly, used for various applications. A few of them are a minimum distance to means method, average distance method, parallelepiped method, maximum likelihood method, modified maximum likelihood method, Baysians method, decision tree classification, and discriminant functions. Decision Rule can be classified into two types: 1- Parametric Decision Rule: A parametric decision rule is trained by the parametric signatures. These signatures are defined by the mean vector and covariance matrix for the data file values of the pixels in the signatures. When a parametric decision rule is used, every pixel is assigned to a class since the parametric decision space is continuous[3] 2-Nonparametric Decision Rule A nonparametric decision rule is not based on statistics; therefore, it is independent of the properties of the data. If a pixel is located within the boundary of a nonparametric signature, then this decision rule assigns the pixel to the signatures class. Basically, a nonparametric decision rule determines whether or not the pixel is located inside of nonparametric signature boundary[3] . 2.3.2 supervised algorithm for image classiffication The principles and working algorithms of all these supervised classifiers are derived as follow : Parallelepiped Classification Parallelepiped classification, sometimes also known as box decision rule, or level-slice procedures, are based on the ranges of values within the training data to define regions within a multidimensional data space. The spectral values of unclassified pixels are projected into data space; those that fall within the regions defined by the training data are assigned to the appropriate categories [1]. In this method a parallelepiped-like (i.e., hyper-rectangle) subspace is defined for each class. Using the training data for each class the limits of the parallelepiped subspace can be defined either by the minimum and maximum pixel values in the given class, or by a certain number of standard deviations on either side of the mean of the training data for the given class . The pixels lying inside the parallelepipeds are tagged to this class. Figure depicts this criterion in cases of two-dimensional feature space[4]. Fig. 3. Implementation of the parallelepiped classification method for three classes using two spectral bands, after[4]. Minimum Distance Classification for supervised classification, these groups are formed by values of pixels within the training fields defined by the analyst.Each cluster can be represented by its centroid, often defined as its mean value. As unassigned pixels are considered for assignment to one of the several classes, the multidimensional distance to each cluster centroid is calculated, and the pixel is then assigned to the closest cluster. Thus the classification proceeds by always using the minimum distance from a given pixel to a cluster centroid defined by the training data as the spectral manifestation of an informational class. Minimum distance classifiers are direct in concept and in implementation but are not widely used in remote sensing work. In its simplest form, minimum distance classification is not always accurate; there is no provision for accommodating differences in variability of classes, and some classes may overlap at their edges. It is possible to devise more sophisticated versions of the basi c approach just outlined by using different distance measures and different methods of defining cluster centroids.[1] Fig. 4. Minimum distance classifier[1] The Euclidean distance is the most common distance metric used in low dimensional data sets. It is also known as the L2 norm. The Euclidean distance is the usual manner in which distance is measured in real world. In this sense, Manhattan distance tends to be more robust to noisy data. Euclidean distance = (1) Where x and y are m-dimensional vectors and denoted by x = (x1, x2, x3 xm) and y = (y1, y2, y3 ym) represent the m attribute values of two classes. [5]. While Euclidean metric is useful in low dimensions, it doesnt work well in high dimensions and for categorical variables. Mahalanobis Distance Mahalanobis Distance is similar to Minimum Distance, except that the covariance matrix is used in the equation. Mahalanobis distance is a well-known statistical distance function. Here, a measure of variability can be incorporated into the distance metric directly. Mahalanobis distance is a distance measure between two points in the space defined by two or more correlated variables. That is to say, Mahalanobis distance takes the correlations within a data set between the variable into consideration. If there are two non-correlated variables, the Mahalanobis distance between the points of the variable in a 2D scatter plot is same as Euclidean distance. In mathematical terms, the Mahalanobis distance is equal to the Euclidean distance when the covariance matrix is the unit matrix. This is exactly the case then if the two columns of the standardized data matrix are orthogonal. The Mahalanobis distance depends on the covariance matrix of the attribute and adequately accounts for the corr elations. Here, the covariance matrix is utilized to correct the effects of cross-covariance between two components of random variable[6, 7]. D=(X-Mc)T (COVc)-1(X-Mc) ( 2) where D = Mahalanobis Distance, c = a particular class, X = measurement vector of the candidate pixel Mc = mean vector of the signature of class c, Covc = covariance matrix of the pixels in the signature of class c, Covc-1 = inverse of Covc, T = transposition function[3]. Maximum Likelihood Classification In nature the classes that we classify exhibit natural variation in their spectral patterns. Further variability is added by the effects of haze, topographic shadowing, system noise, and the effects of mixed pixels. As a result, remote sensing images seldom record spectrally pure classes; more typically, they display a range of brightnesss in each band. The classification strategies considered thus far do not consider variation that may be present within spectral categories and do not address problems that arise when frequency distributions of spectral values from separate categories overlap. The maximum likelihood (ML) procedure is the most common supervised method used with remote sensing. It can be described as a statistical approach to pattern recognition where the probability of a pixel belonging to each of a predefined set of classes is calculated; hence the pixel is assigned to the class with the highest probability [4]MLC is based on the Bayesian probability formula. Bayes Classification: The MLC decision rule is based on a normalized (Gaussian) estimate of the probability density function of each class [8]. Hence, under this assumption and using the mean vector along with the covariance matrix, the distribution of a category response pattern can be completely described [9]. Given these parameters, the statistical probability of a given pixel value can be computed for being a member of a particular class. The pixel would be assigned to the class with highest probability value or be labelled unknown if the probability values are all below a threshold set by the user [10]. Let the spectral classes for an image be represented by à Ã¢â‚¬ °i, i = 1, . . . M Where, M is the total number of classes. In order to determine the class to which a pixel vector x belongs; the conditional probabilities of interest should be followed. P( à Ã¢â‚¬ °i|x), i = 1, . . . M The measurement vector x is a column of Digital Numbers (DN) values for the pixel, where its dimension depends on the number of input bands. This vector describes the pixel as a point in multispectral space with co-ordinates defined by the DNs (Figure 2-20). Fig. 4.Feature space and how a feature vector is plotted in the feature space [9] The probability p(à Ã¢â‚¬ °i |x) gives the likelihood that the correct class is à Ã¢â‚¬ °i for a pixel at position x. Classification is performed according to: x à ¢Ã‹â€ Ã‹â€  à Ã¢â‚¬ °i if p à Ã¢â‚¬ °i |x > p à Ã¢â‚¬ °j |x) for all j à ¢Ã¢â‚¬ °Ã‚   i3 i.e., the pixel at x belongs to class à Ã¢â‚¬ °i if p(à Ã¢â‚¬ °i|x) is the largest. This general approach is called Bayes classification which works as an intuitive decision for the Maximum Likelihood Classifier method [11]. From this discussion one may ask how can the available p(x|à Ã¢â‚¬ °i) can be related from the training data set, to the desired p(à Ã¢â‚¬ °i|x) and the answer is again found in Bayes theorem [12]. From this discussion one may ask how can the available p(x|à Ã¢â‚¬ °i) can be related from the training data set, to the desired p(à Ã¢â‚¬ °i|x) and the answer is again found in Bayes theorem [12]. p (à Ã¢â‚¬ °i|x)= p (x|à Ã¢â‚¬ °i) p (à Ã¢â‚¬ °i )/p(x) 4 Where p(à Ã¢â‚¬ °i ) is the probability that class à Ã¢â‚¬ °i occurs in the image and also called a priori or prior probabilities. And p(x) is the probability of finding a pixel from any class at location x. Maximum Likelihood decision rule is based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes, and that the input bands have normal distributions as in [13] D = ln(ac)-[0.5ln(|Covc|)]-[0.5(X-Mc)T(Cov-1)(X-Mc)] 6 Where: D = weighted distance (likelihood),c = a particular class,X = measurement vector of the candidate pixel, Mc =mean vector of the sample of class c,ac =percent probability that any candidate pixel is a member ofclass c,(Defaults to 1.0, or is entered from a priori knowledge),Covc = covariance matrix of the pixels in the sample of class c,|Covc| = determinant of Covariance (matrix algebra),Covc-1 = inverse of Covariance (matrix algebra) ln = natural logarithm function = transposition function (matrix algebra). 4- Comparison supervised classification techniques: One of the most important keys to classify land use or land cover using suitable techniques the table showed advantages and disadvantages of each techniques [3] : techniques advantage disadvantage Parallelepiped Fast and simple, calculations are made, thus cutting processing Not dependent on normal distributions. Since parallelepipeds have corners, pixels that are actually quite far, spectrally, from the mean of the signature may be classified Minimum Distance Classification Since every pixel is spectrally closer to either one sample mean or another, there are no unclassified pixels. Fastest decision rule to compute, except for parallelepiped Pixels that should be unclassified,, this problem is alleviated by thresholding out the pixels that are farthest from the means of their classes. Does not consider class variability Mahalanobis Distance Takes the variability of classes into account, unlike Minimum Distance or Parallelepiped Tends to overclassify signatures with relatively large values in the covariance matrix. Slower to compute than Parallelepiped or Minimum Distance Maximum Likelihood Most accurate of the classifiers In classification. Takes the variability of classes into account by using the covariance matrix, as does Mahalanobis Distance An extensive equation that takes a long time to compute Maximum Likelihood is parametric, meaning that it relies heavily on anormal distribution of the data in each input band 5- accuracy assessment No classification is complete until its accuracy has been assessed [10]In this context the accuracy means the level of agreement between labels assigned by the classifier and class allocation on the ground collected by the user as test data. To research valid conclusions about maps accuracy from some samples of the map the sample must be selected without bias. Failure to meet these important criteria affects the validity of any further analysis performed using the data because the resulting error matrix may over- or under- estimate the true accuracy. The sampling schemes well determine the distribution of samples across the land scape which will significantly affect accuracy assessment costs [14] When performing accuracy assessment for the whole classified image, the known reference data should be another set of data. Different from the set that is used for training the classifier .If training samples as the reference data are used then the result of the accuracy assessment only indicates how the training samples are classified, but does not indicate how the classifier performs elsewhere in scene [10]. the following are two methods commonly used to do the accuracy assessment derived from table . 1-the Error matrix Table 1.Error matrix[15] Error matrix (table1 ) is square ,with the same number of information classes that will be assessed as the row and column. Numbers in rows are the classification result and numbers in column are ref-erence data (ground truth ).in this square elements along the main diagonal are pixels that are correctly classified. Error matrix is very effective way to represent map accuracy in that individual accuracies of each category are plainly descried along with both the error of commission and error of omission. Error of commission is defined as including an area into acatogary when it does not belong to that category. Error of omission is defined as excluding that area from the catogary in which it truly does belong. Every error is an omission from correct category and commission to a wrong category. With error matrix error of omission and commission can be shown clearly and also several accuracy indexes such as overall accuracy, users accuracy and producers accuracy can be assessed .the fol lowing is detailed description about the three accuracy indexes and their calculation method overall accuracy Overall accuracy is the portion of all reference pixels, which are classified correctly (in the scene) that assignment of the classifications and of the reference classification agree).it is computed by dividing the total number of correctly classified pixels (the sum of the elements along the main diagonal) by the total number of reference pixels. According to the error matrix above the overall accuracy can be calculated as the following: OA == Overall accuracy is Avery coarse measurement. It gives no information about what classes are classified with good accuracy. producers accuracy producer accuracy estimates the probability that a pixel, which is of class I in the reference classification is correctly classified . It is estimate with the reference pixels of class I divided by the pixels where classification and reference classification agree in class I . Given the error matrix above, the producers accuracy can be calculated using the following equation: PA (class I) = Producer accuracy tells how well the classification agrees with reference classification 2.3 users accuracy Users accuracy is estimated by dividing the number of pixels of the classification results for class I with number of pixels that agree with the reference data in class I.it can be calculated as : UA(class I)= Users accuracy predicts the probability that a pixel classified as class I is actually belonging to class I. 2-kappa statistics The kappa analysis is discrete multivariate techniques used in accuracy assessment for statistically determining if one error matrix is significantly different than another (bishop).the result of performing of kappa analysis is KHAT statistics (actually ,an estimate of kappa),which is an- other measure of agreement or accuracy this measure of agreement is based on the difference between the actual agreement in the error matrix(i.e the agreement between the remotely sensed classification and the reference data as indicated by major diagonal) and the chance agreement, which is indicated by the row and column totals(i.e marginal)[16] A detailed comparison between two data sets, one with near-infrared and three visible and the other with the full 8-bands, was made to emphasize the important role of the new bands for improving the separability measurement and the final classification results [17]

Nine Learning Experiences Ages 3-5 Essay

To advance physical and intellectual competence I provide numerous activities to enhance these qualities in the children that I teach. In the area of physical, my goal is to make sure the students are given many possibilities to participate in large motor activities daily. I make this fun by joining them while they are playing. I encourage them to do activities that encourage running and jumping. We play tag, hide and seek, and their favorite game â€Å"Mr. Fox what time is it†. When the weather does not permit us to go outside we have free play inside the gymnasium. We are able to use tricycles, play basketball, and build using pipes and blocks. We also do a unit on how to keep our bodies healthy. This is where we get the parents involved so they can learn what they can do at home with their children. We send home a chart that gives them opportunities to be physically active and they turn this chart in at the end of the unit. Then as a class we discuss what we did with our families. To promote intellectual activities I provide numerous materials that they can use and I rotate them occasionally so they are not bored with it. For example, in our art center the children are exposed to many different types of materials. They have various types of paper and paints as well as recycled material that they can use to sculpt their own art. Our class is also exposed to music of many different cultures. We play music throughout the day and one day a week we have a music class outside of our classroom. As a preschool teacher I keep up with what is new and promote these findings during our learning experiences. I provide many avenues for children to find who they are and respect who others are.

Rhetorical Analysis of Fdr’s First Fireside Chat

A Rhetorical Analysis of Franklin Delano Roosevelt’s First Fireside Chat President Franklin Roosevelt’s â€Å"First Fireside Chat† is a reassuring piece that inspired the nation in a time of need using his voice that projected his personal warmth and charm into the nation’s living rooms to explain the banking crisis. He slowly and comprehensibly informed the American people on what has been done and to explain the complex banking system while using rhetorical appeals of ethos, logos, and pathos to effectively restore American faith in the United States government and banking system.Roosevelt won the 1932 election after a landslide victory over his predecessor Herbert Hoover. At this time, America was going through one of the toughest times inside its own borders ever: The Great Depression. Roosevelt's First Fireside Chat on March 12, 1933 marked the beginning of a series of 30 radio broadcasts to the American people reassuring them the nation was going to recover as he shared his hopes and plans for the country. Roosevelt was simply telling the people what he was doing and why.This level of intimacy with politics made people feel as if they too were part of the administrations decision-making process and many soon felt that they knew Roosevelt personally and most importantly, they grew to trust him. Only eight days after his inauguration, President Roosevelt took to the air waves to let Americans know how the country was doing. Millions gathered around their radios to listen in. The President explained to the country in simple terms why so many banks had failed and why he had decided to close them down on March 6 (the so-called â€Å"bank holiday†.He then described the measures that Congress was taking to make sure that a banking crisis would not happen again. Roosevelt used ethos multiple times in his speech to establish his credibility and honesty. After his introduction Roosevelt told America, â€Å"And I know that when yo u understand what we in Washington have been about, I shall continue to have your cooperation as fully as I have had your sympathy and your help in the past week. This was an effective use of ethos because he draws the distinction between conventional knowledge and new insight that he provides throughout his speech. Another example of effective ethos can be found towards the end of his speech when Roosevelt told America, â€Å"I hope you can see, my friends, from this essential reticle of what your Government is doing that there is nothing complex, nothing radical in the process. † This summed up the knowledge he shared in the banking system and instilled a sense of integrity in the government.Throughout the chat Roosevelt used his knowledge to teach the American banking system and explained thoroughly what went wrong while using some of the most commonly words in the English dictionary, which appealed to a large audience that effectively established personal credibility. Alo ng with ethos, Roosevelt used logos in his speech to successfully show that his plans for America were logical and reasonable. Roosevelt used logos to logically organize his speech to effectively inform America of the Emergency Banking Act and his possible solutions.Roosevelt told America at the beginning of his speech, â€Å"I want to tell you what has been done in the last few days, and why it was done, and what the next steps are going to be. † Roosevelt starts with this sentence because little hope and despair fill American hearts who need explanations for the failure of banks and Roosevelt comes out right away with what he’s about to say. The way Roosevelt structured the speech with upfront communication portrayed him as a very organized and upfront leader.Also in the beginning of his speech he says â€Å"First of all let me state the simple fact that when you deposit money into a bank, the bank does not put the money in a safe deposit vault. † Following th is quote he states exactly what is done with your money and why. Roosevelt used logic to make a statement that everyone can agree with and relate to provide a sense of unity. Roosevelt used logos throughout his speech to show Americans that he is both logical and reasonable.On top of logos, Roosevelt’s most effective rhetorical appeal in his first fireside chat is pathos. He appeals to pathos the most throughout the speech because he wants the concerned citizens to feel comforted. He explains towards the end, â€Å"After all, there is an element in the readjustment of our financial system†¦ and that is the confidence of the people themselves†¦ it is up to you to support and make it work. † This is where he makes the people feel important that draws attention to the audience’s desires to make it out of the banking crisis.Immediately after he inspires in unity as he concludes â€Å"It is your problem, my friends, your problem no less than it is mine. To gether we cannot fail. † Roosevelt used inclusive and emotional language such as â€Å"my friends†, â€Å"we†, and â€Å"together† while referencing to what Americans desire in their future, which made people want to act. Roosevelt used pathos effectively throughout his speech to draw attention to the desires, emotions, and beliefs of the audience.Shortly after Roosevelt was inaugurated on March 4, 1933, Roosevelt addressed a worried nation over radio broadcast in his First Fireside Chat that brought his charm and personal warmth into living rooms. With his effective use of pathos, ethos, and logos his rhetorical appeals helped describe the crisis, restore American confidence and faith, and lay out solutions that he is going to use to fix the problem. Worried and impoverished Americans cherished a voice of hope that called for action when leadership meant the most during the worst economic times our country has faced. Word Count: 974

Transnational Networks And Its Influence On The World On A...

The way socio-political matters are been handled in the 21st century calls for a redefinition of governance and its influence on the world on a global level. NGOs transnational networks are very influential in the enforcement of policy that breaks through states and government. A Government is the governing body of a nation, state, or community and governance the action or manner of governing. Over the years globalization has brought about transnational networks in international politics. What is novel is this networks ability of mobilizing non-state actors with information strategically to help create new issues and persuade over much powerful organizations and government. We in a generation that an issue be it women’s right, human rights, environmental issues becomes a worldwide subject through campaigns internationally. Non-state activist or NGOs normally carry out these campaigns through transnational network advocacy with goals to influence states and international organizations. Globalization has facilitated the growth of these networks and makes new forms of governance imperatives (Naim 2005). The government of states have over the years been faced by pressure of these non state actors to change their policy bringing a whole new definition to the word governance has seen in the 1993 international treaty to ban landmines in which the international NGOs played key roles in making points of environmental degradation and mistreatment of the poor. These were able to putShow MoreRelatedTransnationalism: The Study of Population Moves1737 Words   |  7 Pagesthe study of population movements in a world where improved modes of transport, as well as the images that are transmitted by means of modern telecommunications have shortened the social distance between sending and receiving countries. An immigrants ability to adapt to a different nation has become an easier task than it was before. 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