Presentation on theme: "Spectral contrast enhancement"— Presentation transcript:
1 Spectral contrast enhancement Mirza Muhammad WaqarContact:EXT:2257RG610Course: Introduction to RS & DIP
2 Contents Geographical Information System Remote Sensing & Satellite Image ProcessingColor SpaceLandsat 7 spectral bandsSpectral Reflectance CurvesImage InterpretationSpectral RatioingIndicesThese are the contents of my presentation.
3 OverviewOne of the strength of image processing is that it gives us the abilityTo enhance the view of an area by manipulating the pixels value.Contrast enhancement does not change the values in the image rather simply adjust the colors associated with these color values.
4 Image EnhancementThe alteration of the appearance of an image in such a way that the info contained in that image is more readily interpreted visually in terms of a particular needIt alters the visual impact of the image to improves the info contents for the interpreterThese operations improve the interpretability of an image by changing the contrast between the features in the sceneTo improve the appearance of an image for human visual analysis
5 Image EnhancementNo single standard method can be said to be the best, it depends upon the need of the userThe characteristic of each image in terms of distribution of pixel values over range will change from one area to another , thus enhancement tech suited for one image may not be good for other image covering different type of area
6 Image HistogramHistogram greatly helps to deduce the appearance of an imageIn a dark image, the gray levels would be clustered towards the lower endIn a uniformly bright image, the gray levels would be clustered towards the upper endIn a well contrasted image, the gray levels would be well spread out over much of the range
7 Image EnhancementMethods of improving visual interpretability of an imageBy altering the contrast of an image ( contrast stretching)Converting from black and white to color representationContrast is simply the range and the distribution of the pixel values over the gray scale
8 Perception of ColorsConversion to color is desirable as the eye is more sensitive to variations in hue than change in the brightness
9 Contrast Enhancement/ Stretching Sensors record reflected or emitted radiant flux exiting from earth surface materialsIdeally one material would reflect tremendous amount of energy in a certain wavelength while another much less in the same wavelengthThis would result in contrast between the two types of materialsIn some cases different materials would often reflect similar amount of radiant flux throughout the visible and IR portion of EM spectrum resulting in a relatively low contrast image
10 Contrast EnhancementSensor on board have to be capable of detecting upwelling radiance levels ranging from low (from oceans) to very high (over snow)For particular area to be imaged ,it is unlikely that full dynamic range of the sensor will be used ,thus the corresponding image is dull or over bright-over or under exposed
11 Why we need Contrast Enhancement Quite often the useful data in a digital image populate only a small portion of available range of digital values.Commonly 8 bit or 256 levelsContrast enhancement involves changing the original values so that more of the available range is used.It increases the contrast among the features and their background.
13 Linear Contrast Enhancement This technique involves the translation of the image pixel values from the observed range of digital number to the full range of the display device (e.g. 8 bit)
14 LINEAR STRETCH-MIN MAX The uniform expansion of the of input digital numbers to full range )-255) is called linear stretchBV OUT=255(BV IN-MIN) / (MAX-MIN)MIN=25, MAX=2251301651001351455735255065180215200205225220301201341799614015341133251
16 Histogram Equalization Stretch This stretch assign more display values (range) to the frequently occurring portion of the histogram.In this way, the detail in those areas will be better enhanced having high frequency relative to those areas having low frequency value in the histogram.
17 Histogram ConversionThe histogram of the original image is converted to other types of histograms as specified by userHistogram Stretch- Image values are assigned to the display levels on the basis of their frequency of occurrenceMore display values ( more radiometric details) are assigned to the frequently occurring portion of histogramSpecial Stretch- To analyze specific features in greater radiometric detail s by assigning the display range exclusively to a particular range of image values
20 Standard Deviation Stretch Standard deviation stretch trim all pixels that have a digital number beyond the range the defined standard deviation;Then perform the linear stretch for the remaining pixelsStandard Deviation 1: 67 %Standard Deviation 2: 95 %Standard Deviation 3: 99 %
21 Gaussian StretchThis histogram involve the fitting of the observed histogram to normal or Gaussian histogram.This stretch adjust the range of lookup table values so that the output histogram is approximately a normal distribution.
22 Level Slice StretchIt will slice the input image into user defined number of classes.The output image will have only limited number of variations depending upon the user defined number of classes.
23 Density/ Level Slicing Representation of a range of contiguous gray levels of gray scale image by a single colorUsed to separate the data into “n” intervals or “slices” based on the histogram from one wavelength band.All data within a slice are displayed as one digital number or color in the output imageThe Gray level in the output image corresponds to the number of slicesUsed frequently with thermal images, i.e. different temperature ranges can be shown with different slices
25 Invert Contrast Stretch This contrast enhancement technique invert the current lookup table values.This has the effect of producing a photographic negative of the image.This technique is often used to extract information from the shadow.
26 Gray-Level Thresholding Used to “segment "an input image into two classes.Purpose is to develop a binary mask for one category, so that processing can be applied to each class independentlyOriginal NIR ImageSet threshold hereMask Image for Water
27 Contrast EnhancementIf the range of gray levels could be altered so as to fit the full range of the black and white axis, then the contrast between the dark and bright areas of the image would be improvedDoes not modify the original data unless new file is saved