Digital image processing


Published on

Digital image processing ,Techniques of Digital Image Processing
Image Enhancement ,Image Histogram,Spatial Filtering,Smoothing and Sharpening Examples,Image Classification,Supervised Classification,Unsupervised Classification
remote sensing applications.

Published in: Technology, Business
  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Digital image processing

  1. 1. UNIT III Digital image processing (DIP) It is the task of processing and analyzing the digital data using some image processing algorithm. The analysis of relies only upon multispectral characteristic of the feature represented in the form of tone and color. Most of the common image processing functions available in image analysis systems can be categorized into the following four categories: 1. Preprocessing (Image rectification and restoration) 2. Image Enhancement 3. Image Classification and Analysis 4. Data Merging and GIS Interpretation
  2. 2. Techniques of Digital Image Processing Initial Data Statistics Statistical information such as minimum and maximum values of the data set, mean, standard deviation, and variance for each band are calculated. Histograms and scatter-grams provide a graphical view of the nature of different bands. Image Rectification and Restoration ( or Preprocessing) These are correction needed for the distortion or degradations of raw data. Radiometric and geometric correction are applicable to this. Image Enhancement Purpose of this is to improve the appearance of the imaginary and to assist in subsequent visual interpretation and analysis. Normally, image enhancement involves techniques for increasing the visual distinction between features by improving tonal distinction between various features in a sene using technique of contrast stretching. Image Transformation These are operations similar in concept to image enhancement. Generally, image enhancement operation is carried out on a single band of data, while image transformations are usually on multiple bands. Image Classification The objective of the classification is to replace visual analysis of the image data with quantitative techniques for automating the identification of features in a scene.
  3. 3. Digital Data Initial Display of Image Initial Statistic Extraction Image Rectification and Restoration Image Enhancement Visual Analysis Image Classification Ancillary Data Unsupervised Supervised Classified Output Post processing operation Data Merging Assessment of Accuracy Maps and Images Report Data
  4. 4. Image Enhancement (Histogram Example) Original
  5. 5. Histogram Example (cont. ) Poor contrast
  6. 6. Histogram Example (cont. ) Poor contrast
  7. 7. Histogram Example (cont. ) Enhanced contrast
  8. 8. Image Histogram An image histogram is a graphical representation of the brightness values that comprise an image. The brightness values (i.e. 0-255) are displayed along the x-axis of the graph and the frequency of occurrence of each of these values in the image on the Yaxis. By manipulating the range of digital values in an image, i.e. graphically represented by its histogram, various enhancement can be applied to the data. However, these can be grouped under two categories: 1. Linear contrast Enhancement 2. Non linear contrast Enhancement
  9. 9. Spatial Filtering Spatial filtering is the digital processing function that are used to enhance the appearance of an image. Spatial features are designed to highlight or suppress specific features in an image based on their spatial frequency. Spatial frequency is related to the concept of image texture. It refers to the frequency of the variations in tone that appear in an image. Rough texture areas of an image, where the changes in tone are abrupt over a small area, have high spatial frequencies, while smooth areas with little variation in tone over several pixels, have low spatial frequencies. Types of Filters 1. Low-pass Filter: is designed to emphasize large homogenous areas of similar tone and reduce the smaller detail in an image. Thus, these filters generally serve to smooth the appearance of an image. 2. High-pass Filter: such filters do the opposite job as low-pass filter. They are served to sharpen the appearance of fine details in an image. Other Filters 1. High boost filters 2. Directional or edge detection filters
  10. 10. Smoothing and Sharpening Examples Smoothing (Low-pass Filter) Sharpening (High-pass Filter)
  11. 11. Image Classification Purpose • To identify and map areas with similar characteristics • To assign meaningful categories such as land-use or land-cover classes to pixel values Classification Methods 1. Supervised classification 2. Unsupervised classification
  12. 12. Supervised Classification In this classification method, an analyst identifies the imaginary in terms of homogenous representative samples of different surface cover type of interest. These samples are called as “Training Areas”. The selection of appropriate training area is based on the analyst’s familiarity with geographical area and knowledge of the actual surface cover types present in the image. The numerical information in all spectral bands for the pixels comprising these areas are used to train the computer to recognize specially similar areas for each class. Therefore, in supervised classification, the analyst is first identifies the information classes based on which it determines the spectral classes which represent them. Common Classifiers: 1. Parallel-piped classifiers 2. Minimum distance to mean classifiers 3. Maximum likelihood classifiers (MLC)
  13. 13. Supervised Classification Supervised classification requires the analyst to select training areas where he/she knows what is on the ground and then digitize a polygon within that area… The computer then creates... Mean Spectral Signatures Conifer Known Conifer Area Water Known Water Areac Deciduous Known Deciduous Area Digital Image
  14. 14. Supervised Classification Mean Spectral Signatures Multispectral Image Information (Classified Image) Conifer Deciduous Water Unknown Spectral Signature of Next Pixel to be Classified
  15. 15. Unsupervised Classification Unsupervised classification reverses the supervised classification process. Spectral classes are grouped first, based only on the numerical information in the data and are then matched by the analyst to information classes. Programs called Clustering algorithms are used to determine the natural groupings or structures in the data. Usually, the analyst specify how many groups or clusters are to be looked for in the data. In addition to specifying the desired number of classes, the analyst may also specify the parameters related to separation distance among the clusters and variation with each cluster However, algorithm for this classification operates in a two- pass mode. In the first pass, the algorithm sequentially builds class clusters. In second pass, a minimum distance classifier is applied to the whole data set on a pixel-by-pixel basis, where each pixel is assigned to one of the mean vectors created in pass 1 mode.
  16. 16. Unsupervised Classification The analyst requests the computer to examine the image and extract a number of spectrally distinct clusters… Spectrally Distinct Clusters Cluster 3 Cluster 5 Cluster 1 Digital Image Cluster 6 Cluster 2 Cluster 4
  17. 17. Unsupervised Classification Saved Clusters Cluster 3 Cluster 5 Cluster 1 Output Classified Image Cluster 6 Cluster 2 Next Pixel to be Classified Cluster 4 Unknown
  18. 18. Unsupervised Classification The result of the unsupervised classification is not yet information until… The analyst determines the ground cover for each of the clusters… ??? Water ??? Water ??? Conifer ??? Conifer ??? Hardwood ??? Hardwood
  19. 19. Remote Sensing Applications Land Use/Land Cover mapping 1. Natural resource management 2. Wildlife protection 3. Encroachment Urban Planning Forestry & Ecosystem 1. Land parcel mapping 1. Forest cover & density mapping 2. Infrastructure mapping 2. Deforestation mapping 3. Land use change detection 3. Forest fire mapping 4. Future urban expansion planning 4. Wetland mapping and monitoring Agriculture 1. Crop type classification 2. Crop condition assessment 3. Crop yield estimation 4. Mapping of soil characteristic 5. Soil moisture estimation 5. Biomass estimation 6. Species inventory
  20. 20. Remote Sensing Applications ……cont. Geology 1. Lithological mapping 2. Mineral exploration 3. Environmental geology Hydrology 4. Sedimentation mapping and monitoring 1. Watershed mapping & management 5. Geo-hazard mapping 2. Flood delineation and mapping 6. Glacier mapping 3. Ground water targeting Ocean applications 1. Storm forecasting Other Applications 2. Water quality monitoring 1. Flood Plain Mapping 3. Aquaculture inventory and monitoring 2. Disaster Management 4. Navigation routing 3. District level Planning 5. Coastal vegetation mapping 6. Oil spill
  21. 21. Land Use And Land Cover Mapping A study on land use and land cover for a part of Hraidwar district was carried out for the area lying LISS III PAN between 78007’13” E and 78016’14” E longitude and 300 N and 30008’53” N latitudes covering an area of Initial Statistics nearly 260 km2. IRS-1C LISS III of April 3, 2000 was used along with Contrast Enhancement PAN image of the same date. The methodology adopted is shown in figure. Registration On the basis of field visit, 11 cities were identified. These classes are: i). Thin forest ii). Medium forest iii). Dense forest vi). Open land Supervised Classification iv). Fallow land v). Shrubs Ground Data Classified Image vii). Shallow water viii). Wet land ix). Dry sand xi). Deep water x). Built-up-area Accuracy Assessment
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.