T10-Spatial Filtering and Texture Analysis_v1_3slides

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Unformatted text preview: 1 Geography 333 Remote Sensing I Topic 10: Spatial Filtering and Texture Analysis 2 Readings Topic 10: Chapter 8 Image Enhancement Topic 11: Chapter 9 Classification & Thematic Information Extraction: Pattern Recognition pg 337-389 Topic 12: GEOBIA GEOBIA reading (Hay and Castilla, 2008) on BB. Topic 13: Chapter 13 Thematic Map Accuracy Assessment 3 Course Business: 25% Mid-term Nov 02 (in-class) Updated Outline: Guest lectures: Nov 25 Mr. Mustafiz Rhaman (Change Detection) Dec 02 TBA 2 4 Topics Spatial filtering Convolution Fourier analysis Local window texture analysis First-order texture statistics Second-order (GLCM) texture analysis Structural Texture Primitive Neighbourhood analysis The remote sensing scene model: Your guide to image processing in the spatial domain 5 The Elements of Image Interpretation Shape General form, configuration, or outline Size In context of the image scale; relative or absolute Pattern Spatial arrangement or repetition of objects Tone/Hue Relative brightness or colour of objects (Image) Texture Characteristic repetition of tone or colour, thus spatial variability of tone/colour Site Geographic or topographic location Association Occurrence of features in relation to others Shadows 6 Spatial Filtering Just as spectral image enhancements are designed to broaden the spectral information content, spatial filtering techniques are designed to emphasize or de- emphasize information in the spatial domain High frequency Low frequency 3 7 High-Pass and Low Pass Filters Low-pass filters remove the high frequency information (noise) and show general trends in the data High-pass filters remove the low frequency information and show the high frequency 8 Spatial Filtering Purposes Improve the interpretability of image data Aid in feature extraction Remove or reduce sensor degradation Methods Convolution Fourier analysis 9 Convolution The action of coiling or twisting or winding together Convolution involves the passing of a moving window over an image and the creation of a new image Each pixel in the new image is a function of the original pixel values within the moving window and the coefficients of the moving window as specified by the user Also known as moving window or local operations 4 10 Operation of Moving Windows Edge pixels averaged or removed from the raster 11 Pixel Values Averaged or Removed From Raster 12 Convolution Filtering: Edge Effects 5 13 Convolution Coefficients The moving window (kernel) is a matrix of convolution coefficients, commonly 3x3, 5x5, or 7x7 pixels in size 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1 14 Mean (Low Pass) Filter 183 183 185 180 159 180 180 174 158 131 174 165 150 128 107 153 140 122 105 93 130 118 105 94 90 178 181 186 192 177 179 179 187 175 122 179 177 171 120 92 166 142 107 91 89 114 106 95 92 89 1/9 1/9 1/9 1/9 1/9...
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This note was uploaded on 01/18/2011 for the course GEOG 331 taught by Professor Staff during the Fall '08 term at Kansas.

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T10-Spatial Filtering and Texture Analysis_v1_3slides - 1...

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