Discuss the image processing used to analyze satellite images

MASINDE MULIRO UNIVERSITY OF SCIENCE AND TECHNOLOGY REG NO: COM/0030/12 COURSE: GEOGRAPHICAL INFORMATION SYSTEM TASK: CAT LECTURER: MR. MAKHULO QUESTION Discuss the image processing used to analyze satellite images; Raster GIS is used to explore variety of geographical modelling, spatial and data presentation techniques. PART 1-IMAGE PROCESSING USED TO ANALYZE SATELLITE IMAGES INTRODUCTION Pictures are the most effective means of conveying information. A picture is worth a thousand words. Pictures concisely convey information about positions, sizes and interrelationships between objects. Human beings are good at deriving information from such images, because of our innate visual and mental abilities. About 75% of the information received by human is in pictorial form. This discussion will be focusing on analysis of remotely sensed images. These Images are represented in digital form. When represented as numbers, brightness can be added, subtracted, multiplied, divided and, in general, subjected to statistical manipulations that are not possible if an image is presented only as a photograph. Previously, digital remote sensing data could be analyzed only at specialized remote sensing laboratories. Specialized equipment and trained Personnel necessary to conduct routine machine analysis of data were not widely available, in part because of limited availability of digital remote sensing data and a lack of appreciation of their qualities. This document describes the basic technological aspects of digital image processing with special reference to satellite image processing. Basically all satellite images processing information can be grouped into three categories. i. Image rectification and restoration ii. Image enhancement iii. Information extraction I. IMAGE RECTIFICATION & RESTORATION Rectification is a process of geometrically correcting an image so that it can be represented on a planar surface, conform to other images or conform to a map. That is, it is the process by which geometry of an image is made plan metric. It is  necessary when accurate area, distance and direction measurements are required to be made from the imagery. It is achieved by transforming the data from one grid system into another grid system using a geometric transformation. Ground Control Points (GCP) are the specific pixels in the input image for which the output map coordinates are known. By using more points than necessary to solve the transformation equations a least squares solution may be found that it minimizes the sum of the squares of the errors. Care should be exercised when selecting ground control points as their number, quality and distribution affect the result of the rectification. Once the mapping transformation has been determined a procedure called re sampling is employed. Re sampling matches the coordinates of image pixels to their real world coordinates and writes a new image on a pixel by pixel basis. Since the grid of pixels in the source image rarely matches the grid for the reference image, the pixels are re sampled so that new data file values for the output file can be calculated. II. IMAGE ENHANCEMENT Image enhancement techniques improve the quality of an image as perceived by a human. These techniques are most useful because many satellite images when examined on a color display give inadequate information for image interpretation. There exists a wide variety of techniques for improving image quality. The contrast stretch, density slicing, edge, enhancement, and spatial filtering are the more commonly used techniques. Image enhancement is attempted after the image is corrected for geometric and radiometric distortions. Image enhancement methods are applied separately to each band of a multispectral image. Digital techniques have been found to be most satisfactory than the photographic technique for image enhancement, because of the precision and wide variety of digital processes. Issues associated with image enhancement include;  Contrast-Contrast generally refers to the difference in luminance or grey level values in an image and is an important characteristic.  Contrast Enhancement-Contrast enhancement techniques expand the range of brightness values in an image so that the image can be efficiently displayed in a manner desired by the analyst.  Spatial Filtering-A characteristic of remotely sensed images is a parameter called spatial frequency defined as number of changes in Brightness Value per unit distance for any particular part of an image.  Spatial filtering is the process of dividing the image into its constituent spatial frequencies and selectively altering certain spatial frequencies to emphasize some image features. This technique increases the analyst’s ability to discriminate detail. III. INFORMATION EXTRACTION Image Classification The overall objective of image classification is to automatically categorize all pixels in an image into land cover classes or themes. Normally, multi spectral Data are used to perform the classification and the spectral pattern present within the data for each pixel is used as numerical basis for categorization. That is, different feature types manifest different combination of Digital Numbers based on their inherent spectral reflectance and emittence properties. The term classifier refers loosely to a computer program that implements vary so greatly. Therefore, it is essential that the analyst understands the alternative Strategies for image classification. The traditional methods of classification mainly follow two approaches: unsupervised and supervised. The unsupervised approach attempts spectral grouping that may have an unclear meaning from the user’s point of view. Having established these, the analyst then tries to associate an information class with each group. The unsupervised approach is often referred to as clustering and results in statistics that are for spectral, statistical clusters. In the supervised approach to classification, the image analyst supervises the pixel categorization process by specifying to the computer algorithm; numerical descriptors of the various lands cover types present in the scene. To do this, representative sample sites of known cover types, called training areas or training sites, are used to compile a numerical interpretation key that describes. It has been found that in areas of complex terrain, the unsupervised approach is preferable to the supervised one. In such conditions if the supervised approach is used, the user will have difficulty in selecting training sites because of the variability of spectral response within each class. Additionally, the unsupervised approach has the potential advantage of revealing discriminable classes unknown from previous work. However, when definition of representative training areas is possible and statistical information classes show a  close correspondence, the results of supervised classification will be superior to unsupervised classification. PART 2-RASTER GIS What is raster data? In its simplest form, a raster consists of a matrix of cells (or pixels) organized into rows and columns (or a grid), as shown in the graphic below, where each cell contains a value representing information, such as temperature. Raster are digital aerial photographs, imagery from satellites, digital pictures, or even scanned maps. Data stored in a raster format represent real‐world phenomena such as:  Thematic data (also known as discrete data), representing features such as land‐use or soils data. LANDFIRE data layers depicting fuels, vegetation, fire regimes, and other features are also examples of this.  Continuous data representing phenomena such as temperature and elevation data or spectral data, including, for example, satellite images and aerial photographs.  Pictures; examples include scanned maps or drawings and building photographs. Why store data as a raster? Sometimes there is no choice as to how data are stored; for example, imagery may only be available as a raster. However, there are many other features (such as points) and measurements (such as rainfall) that could be stored as either a raster or a feature (vector) data type. Following is a list of the advantages of storing data as a raster: A simple data structure—A matrix of cells with values representing a coordinate and sometimes linked to an attribute table. i. A powerful format for advanced spatial and statistical analysis. ii. The ability to represent continuous surfaces and perform surface analysis. iii. The ability to uniformly store points, lines, polygons, and surfaces. iv. The ability to perform fast overlays with complex data set. REFERNCES 1. Processing Of Satellite Image Using Digital Image Processing By B.Sreenivas &B.Narasimha Chary 2. Digital Image Processing by Rafael C. Gonzalez & R.E.Woods 3. Campbell, J.B. 1996. Introduction to Remote Sensing, Taylor & Francis, London. 4. Suitability Analysis with Raster Data by Chris Wayne. 5. Spatial Analysis of Raster Data URL:www.serc.carleton.edu/NAGTworkshops/gis/activities2/48022.html