Digital Image Processing Final Material
Digital Image Processing Final Material GPY 470
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This 5 page Study Guide was uploaded by Maddy Moldenhauer on Tuesday April 26, 2016. The Study Guide belongs to GPY 470 at Grand Valley State University taught by Sun in Winter 2016. Since its upload, it has received 8 views. For similar materials see Digital Image Processing in Geography at Grand Valley State University.
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Date Created: 04/26/16
Maddy Moldenhauer GPY470 Final Exam PART 1 Digital Image Processing Procedure 1. Ground Receiving station and data ordering: The ground station receives the data sent from the aerial remote sensor back to Earth’s surface. The reflected energy from Earth’s surface is data that is acquired by the remote sensor and is transmitted to the ground station for storage and analysis. This data is then processed by the systems and software at the ground station so that the scientists and geographers can put the data to use. 2. Radiometric preprocessing (Calibration & Atmospheric Correction): Radiometric corrections are carried out to address variations/errors that occur in the measured brightness values of pixels due to sensor errors (line dropouts, striping or banding), atmospheric effects (scattering or absorption of electromagnetic radiation), or topographic effects. 3. Geometric preprocessing: Images are rarely provided in the correct projection, coordinate system and free of geometric distortions. Geometric distortions occur due to Earth rotation during image acquisition and due to Earth curvature. Correcting for these geometric distortions are the use of ground control points (GCPs) to recognize points on a map in the image to model the distortion and to define the transformation between the image and the map. The cocoordinates resulting from a transformation are an estimate and need to be checked for their degree of accuracy, which is normally express for each control point and overall root mean square (and RMS error). 4. Feature Extraction (Image Enhancement): Remotely sensed images can go through both spectral and spatial enhancement during processing and analyzation. Spectral enhancement/image transformation enhances images by transforming the values of each pixel on a multiband basis. Spatial enhancement improves images based on the values of individual, neighboring pixels and deals largely with spatial frequency (the difference between the highest and lowest values of a contiguous set of pixels). This process can be done by using spatial convolution filtering. These enhancement processes are completed to increase the quality of the image for the specific purpose that it is being used for. 5. Image Classification for category variables: The remotely sensed images are analyzed for the different variables that they contain. These variables are then separated into specific classes, usually based on the land use/cover type. Image classification is the process that assigns pixels in an image to these specific classes to generate a thematic map. This process includes designing a classification scheme, choosing a classification method (supervised/unsupervised classification), and then preforming an accuracy assessment. This process can also take into account expert, subpixel, and objectoriented image segmentation and classification. 6. Estimation of bio/geophysical variables: Remotely sensed images have bio/geophysical variables that need to be taken into consideration, including vegetation, surface temperature, soil moisture, surface roughness, the atmosphere, land use, vegetation stress, etc. Some important environmental characteristics that play a role in remote sensing include atmospheric conditions (cloud cover, humidity, etc), soil moisture conditions, characteristics of the phenological cycle, and tidal stages. These all are important factors to take into account to produce remote sensing images that are higher in quality for later analysis and usage for research. 7. Insitu data collection and validation: Insitu data is data that is collected directly in the field and is also known as in place data collection. These measurements are not affected by the atmosphere and are collected using a spectroradiometer. This data is then compared to the remotely sensed data for validation and evaluation. Classes and land use/cover types are typically the type of data that is validated during this process to make sure that the remotely sensed data is accurate and representative of the actual land uses. This process investigates the information in the physical landscape to provide accurate background information for further usage. 2.44 2.44 2.78 2.56 2.56 PART 2 2.44 2.44 2.78 2.56 2.56 1) LowPass Filter 2.89 2.89 3.22 2.89 2.89 2.89 2.89 4 4.11 4.11 2.89 2.89 4 4.11 4.11 (1x1) + (7x1) + (2x1) + (1x1) + (2x1) + (3x1) + (2x1) + (2x1) + (2x1) = 22/9 = 2.4444 (7x1) + (2x1) + (2x1) + (2x1) + (3x1) + (2x1) + (2x1) + (2x1) + (3x1) = 25/9 = 2.7778 (2x1) + (2x1) + (6x1) + (3x1) + (2x1) + (1x1) + (2x1) + (3x1) + (2x1) = 23/9 = 2.5556 (1x1) + (2x1) + (3x1) + (2x1) + (2x1) + (2x1) + (2x1) + (8x1) + (4x1) = 26/9 = 2.8889 (2x1) + (3x1) + (2x1) + (2x1) + (2x1) + (3x1) + (8x1) + (4x1) + (3x1) = 29/9 = 3.2222 (3x1) + (2x1) + (1x1) + (2x1) + (3x1) + (2x1) + (4x1) + (3x1) + (6x1) = 26/9 = 2.8889 (2x1) + (2x1) + (2x1) + (2x1) + (8x1) + (4x1) + (1x1) + (3x1) + (2x1) = 26/9 = 2.8889 (2x1) + (2x1) + (3x1) + (8x1) + (4x1) + (3x1) + (3x1) + (2x1) + (9x1) = 36/9 = 4 (2x1) + (3x1) + (2x1) + (4x1) + (3x1) + (6x1) + (2x1) + (9x1) + (6x1) = 37/9 = 4.1111 .22 .22 .56 .33 .33 2) HighPass Filter .22 .22 .56 .33 .33 .67 .67 1 .44 .44 6 6 .44 .78 .78 6 6 .44 .78 .78 (1x1) + (7x1) + (2x1) + (1x1) + (2x9) + (3x1) + (2x1) + (2x1) + (2x1) = 2/9 = .2222 (7x1) + (2x1) + (2x1) + (2x1) + (3x9) + (2x1) + (2x1) + (2x1) + (3x1) = 5/9 = .5556 (2x1) + (2x1) + (6x1) + (3x1) + (2x9) + (1x1) + (2x1) + (3x1) + (2x1) = 3/9 = .3333 (1x1) + (2x1) + (3x1) + (2x1) + (2x9) + (2x1) + (2x1) + (8x1) + (4x1) = 6/9 = .6667 (2x1) + (3x1) + (2x1) + (2x1) + (2x9) + (3x1) + (8x1) + (4x1) + (3x1) = 9/9 = 1 (3x1) + (2x1) + (1x1) + (2x1) + (3x9) + (2x1) + (4x1) + (3x1) + (6x1) = 4/9 = .4444 (2x1) + (2x1) + (2x1) + (2x1) + (8x9) + (4x1) + (1x1) + (3x1) + (2x1) = 54/9 = 6 (2x1) + (2x1) + (3x1) + (8x1) + (4x9) + (3x1) + (3x1) + (2x1) + (9x1) = 4/9 = .4444 (2x1) + (3x1) + (2x1) + (4x1) + (3x9) + (6x1) + (2x1) + (9x1) + (6x1) = 7/9 = .7778 3) Focal Analysis Sum 22 22 25 23 23 22 22 25 23 23 26 26 29 26 26 26 26 36 37 37 26 26 36 37 37 1+7+2+1+2+3+2+2+2 = 22 2+2+2+2+8+4+1+3+2 = 26 7+2+2+2+3+2+2+2+3 = 25 2+2+3+8+4+3+3+2+9 = 36 2+2+6+3+2+1+2+3+2 = 23 2+3+2+4+3+6+2+9+6 = 37 1+2+3+2+2+2+2+8+4 = 26 2+3+2+2+2+3+8+4+3 = 29 3+2+1+2+3+2+4+3+6 = 26 4) Focal Analysis Max I took the maximum value for 7 7 7 6 6 each 3x3 square and inputted 7 7 7 6 6 the maximum value in the center square of the 3x3 grid 8 8 8 6 6 8 8 9 9 9 in the image 8 8 9 9 9
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