This paper presents a image fusion technique based on PCA and fuzzy logic. the framework of the proposed image fusion technique is divided in the following major phases: preprocesing phase Feature extraction based on the principal component analysis The image fusion based on fuzzy set Reconstruction final image The figure (1) shows the framework of the proposed image fusion and its phases. Fig. 1. The proposed approach of image fusion phases A. Preprocessing Phase This phase consists of three steps registration , resampling and histogram matching .In the following 1) Registration:Image fusion is the approach of combining two or more images of same scene to obtain the more informative image. The image data is recorded by sensors on satellites; it may be contained errors in geometry which may be caused by the rotation of the earth during image collection. So the images must be registered. The registration is the preprocessing step in the fusion framework. Registration is the overlaying two images or more of the same scene taken at different times or by different sensors . The registration is a crucial step in many image analysis tasks like image fusion, change detection, ect. In this paper; the ground control point technique is used to register the MS images to the Pan image as reference image. The ground control points method is described as the points on the earth of known location used as georeference for the scene image. All MS images in this paper are registered and The Pan image is used as reference image; see figure 2. (a) The MS image before reg-isteration. (b) The MS image after regis-teration . Fig. 2. The impact of Registration on satellite images 2) Resampling: The resampling is crtical step in prepro-cessing the... ... middle of paper ... ...CA is used to calculate the first component analysis to redundant the information and focus on pc 1 which has the common spatial information in the multispectral images. While the spectral information that is specific for each multispectral image lies on the others PCs. the multispectral images are used as the input data for PCA to obtain the pc 1 which is used as input to fuzzy set.The algorithm (1) shows the main steps of the principal components analysis. Algorithm 1: The principal components analysis algorithm 1: Input: the MS images (3 bands) in the matrix form. %Perform PCA using covariance. 2: data - MxN matrix of input data 3: Reshape 3 bands into 1*(m*n) 4: Subtract the mean 5: Calculate covariance matrix 6: Get eigenvalues and eigenvectors of matrix covariance 7: Fetch the first principal component( PC 1 ) 8: Output: principal component(PC1, PC 2 and PC 3 )
To compare the mean and variance of the Landsat TM and SPOT 5 HRG FPC Time Series for specific land cover types based on vegetation communities; and
Stephen V. Stehman, “Selecting and interpreting measures of thematic classification accuracy”. Remote Sensing of Environment, Vol. 62, No.1, pp.77–89, 1997.
In particular, I have special interest in focusing in Agriculture, due to my home region mainly has an agricultural profile. During my training I realized the importance of reliable and quality information sources. Similarly, I recognize in satellite and aerial imagery a rich source of information. Specifically, in the future I would like to exploit this type of data for the study of soil quality and crop performance in order to unveil patterns that allow us to better understand their features and shortcomings.
Steele, Lisa J. "The View From on High: Satellite Remote Sensing Technology and the Fourth
While real-time satellite imaging is not entirely available, it is only a matter of time until this new technology is easily and readily available for the masses. The backing of the second largest corporation, Alphabet Inc., in the world proves the reality of such technology as fact rather than just science fiction. You, Mark, are the VP of Operations and Development of Terra Bella and were recently informed by the CEO regarding a military offer to buy all rights to the technology. The CEO is asking you to be on the board responsible for determining the future of the company and determine the
Abstract--- Biometrics covers a variety of technologies in which unique identifiable attributes of people are used for identification and authentication. Palm print recognition system is widespread bio-metric authentication systems. A palm print is the feature pattern on the basis of their edges. Each person has his own palm prints with the permanent uniqueness. The common problem for palm print recognition is finding the minutiae by its local features and edges. Rotation and location invariant of the different palm prints images is also a major problem for recognizing the actual palm print image. There is need to overcome form these difficulties and to work over these areas. The given paper gives the comprehensive review of Palm Print recognition
Principal Component Analysis (PCA) is a multivariate analysis performed in purpose of reducing the dimensionality of a multivariate data set in order to recognize the shape or pattern of that data set. In other words, PCA is a powerful technique for pattern recognition that attempts to explain the variance of a large set of inter-correlated variables. It indicates the association between variables, thus, reducing the dimensionality of the data set. (Helena et al, 2000; Wunderlin et al, 2001; Singh et al, 2004)
...regarded GPS – an indispensable part of GIS. Discussions on cartographic principles, commercial GIS software programs, satellite images, aerial photos, and geodatabases are some of the other conspicuous omissions in this book. There is an inconsistency in the depth of topics explored; for example map projections are explored in great depth, while vector topology is merely glossed over. These omissions and inconsistencies would in my opinion make this book marginally less beneficial to all the three audiences together. However, there is something for all them; structure for engineers, equations for engineers and students, and GIS concepts for students, engineers and users. This book will therefore be undeniably valuable if used to complement the material in some of the other fundamental GIS books in the discipline. It has merits, but there is room for improvement.
When the complete set of principal component variables Y is given, it is found that a MEWMA chart applied to Y generates the same value of T^2 as applying MEWMA to original variables, X. [6]
The following is a brief illustration of the principles of GPS. For more information see previous chapter. The Global positioning System (GPS) is a satellite-base navigation system that provides a user with proper equipment access to positioning information. The most commonly used approaches for GPS positioning are the Iterative Least Square (ILS) and the Kalman Filter (EKF) methods. Both of them are based on psuedorange equation:
This approach includes two processes, training and classification (Chelali, Djeradi & Dejradi, 2009). In the training process, a subspace will be established by using the training samples, and then the training faces will be projected onto the same subspace. In the classification process, the input face image will be measured by Euclidean Distance to the subspace, and a decision will be made, either accept or reject.
some of the applications are meteorology,oceanography,biodiversity conservation,cartography,regional planning,warfare etc.interpretation of satellite imagery is conducted by using special techniques known as remote
Correlation- based method: It uses richer gray scale information. It overcome problems of above method, it can work with bad quality data. But it has some of its own problems like localization of points.
The Global Positioning System, more commonly called the GPS is a satellite based system that provides navigation for almost everything from cell phones to automobiles. This wonderful technology is very vital in today’s economy because of its prominence in banking, financial markets, power grids, farming, construction and so much more. It also protects human life by preventing accidents, helping in search and rescue missions and is critical to nearly every facet of military operations. There are three segments that make up the global positioning system: the space segment, the control segment and the user segment. The segment we are familiar with is the user segment. The user segment is what receives GPS signals, determines the distance between a satellite and a receiver and solves the navigation equations, all in order to obtain the coordinates of a specific place. The space segment consists of 31 satellites but there is an availability of at least 24 satellites that are approximately 6 000-12 000 miles above the earth.
The Global Positioning System consists of three sections, 1.satellites which are orbiting the planet, 2.there are numerous control/monitoring centers here on the ground, and 3. gps receivers which are used by their owners. The satellites send down signals from orbit, which are received by GPS receivers on the ground in the air or on the water, the GPS receiver then converts this information into a location longitude, latitude and altitude along with time.