Digital Image Processing
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Digital Image Processing

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For Remote Sensing & GIS

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Digital Image Processing Digital Image Processing Presentation Transcript

  • Digital Image Processing Lokhaze Ali
  • Image• An image can be defined as a two- dimensional signal (analog or digital), that contains intensity (grayscale), or color information arranged along an x and y spatial axis.
  • Digital Image Processing• Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build- up of noise and signal distortion during processing.
  • PixelsThe discrete elements that make up a digital image are called PictureElements or Pixels
  • Sampling RateThe sampling rate must be high enough to capture the required detail
  • Range of colors of Gray
  • Gaussian Blur Filter Effects Embosing Filter
  • Brightness Transform ResultGrayscale Conversion Result
  • Brightness Transform Results Contrast Transform Results
  • Image ResizingTo shrink an image to half its original size, we must discard half ofthe image’s pixel information. To accomplish, throw out every otherrow and column in the image.
  • Image Noise Examples
  • Resizing AlgotithmColor Interpolation in Stretching Image
  • Medical Image Processing
  • Medical Image Processing
  • Ultrasound
  • Weather Forecast - Tsunami
  • Atmospheric Study - OzoneThis shows the thickness of the Earths ozone layer on January 27thfrom 1982 to 2012. This atmospheric layer protects Earth fromdangerous levels of solar ultraviolet radiation. The thickness is measuredin Dobson units, in this image, smaller amounts of overhead ozone areshown in blue, while larger amounts are shown in orange and yellow.
  • Motion Detector Cameras
  • Number of Rotations
  • Image Compressions
  • GradientShop: A novel approach to image and video processing• http://www.youtube.com/watch?v=MDa6NB HAMfg
  • Tomorrow’s Vision• http://www.youtube.com/watch?v=SPlxnYMZ Kww&feature=related
  • Karst• Karst is a characteristic geological feature of areas comprised of limestone. Due to the solubility of these rocks in water, exhibit an extreme heterogeneity of hydraulic conductivities. The characterizing features of karst aquifers are the open conduits, which provide low resistance pathways for ground water flow. Overall cave orientation is largely controlled by hydraulic gradient, joint patterns and other tectonic features, such as faulting and folding. The karst depressions may form on the surface by subsurface actions (dissolution and collapse). Thus, the depressions often show regularity of pattern or alignments, frequently in association with structurally guided cave systems below.
  • Karst Geomorphological Analysis
  • Applications in Remote Sensing
  • . Applications in Remote Sensing Quickbird band ratios for spectral enhancement of the land surface classification. (a) A region of interest with several meso- and micro-sized karst depressions (brownish colors; white arrows). (b) The iron oxide index (bands 3 by 1) that enhances the separability of end members in classifications. Locations rich in corresponding minerals are explicitly highlighted by high grey values (middle; white colors). (c) The infrared-red index (bands 4 by 3), which allows improved differentiation between vegetation types (bright colors) and non-vegetated areas (dark tones). It is possible to detect karst depressions covered by plant species like grassland (white ellipses). In order to conduct a precise karst landform mapping, the latter must also be integrated into the desired indirect variable for depressions along with bare sediment
  • Applications in Remote Sensing • Subsets of DEM derivatives (SRTM, left column; ASTER, right column). (a and b) Slope. (c and d) Aspect. (e and f) Curvature. A large pole, which can be detected and compared on every image, is marked by white circles. Larger morphologic units, such as valleys, are generally displayed quite well (e.g. a, upper left), whereas micro- and meso-relief landforms fall below the resolving power of the data.
  • Applications in Remote Sensing • Comparison of different remote sensing data with regard to spatial resolution. All satellite image subsets are displayed as false color composites (a, b and d). The Landsat ETM+ imagery (a; 30 m resolution) fails to highlight small karst depressions. Only assumptions about existing landforms can be made. In comparison, ASTER satellite data (b) are more suitable due to their twofold higher pixel size (15 m) but still perform poorly as it is unclear if associated pixels represent sinkholes or just sparse vegetation clusters (red colors). The scanned black and white aerial photograph (c; spatial resolution about 0.5 m; contrast enhanced) provides detailed information about karst depressions filled with sediment (darker tones). After data fusion to 0.61 m ground sample distance (gsd), Quickbird imagery (d) ideally qualifies for karst feature
  • Hydrological Surface Analysis
  • Karst Geomorphological Map