![]() Digital Image Processing Techniques
![]() Introduction to Image ProcessingThe digital analysis of data, more specifically of remote sensing
digital imagery from orbital platforms, has seen in the last 25 years a
great deal of developments of techniques aimed at the analysis of
multidimensional data, acquired by a number of different sensors. These
techniques have received the name of digital
image processing. By digital image processing we
mean the manipulation of an image by a computer in such a way that both
the input and the output are images. By comparison, in the pattern
recognition discipline the input is an image while the output consists
of a classification or a description of this image. On the other hand,
the area of computer graphics consists of the generation of images
starting from a description of them. The
objective of using digital image processing is to enhance the visual
aspect of certain structural features in the image for the human
analyst and also to provide other subsidies to its interpretation,
including the generation of products
that could later undergo
further processing. The use of multispectral images obtained by satellites like Landsat, SPOT, ERS1, NOAA, and others, have proved to be a powerful technique for many applications in the research of natural resources. The acquisition of spectral information obtained by the systems in different parts of the electromagnetic spectrum, aimed at the identification and discrimination of targets, depend basically on the quality of the data representation contained in the images. The digital processing techniques available in SPRING are better suited to the images generated by multispectral sensors. The
techniques of digital image processing (DIP), besides allowing the
analysis of a scene in various regions of the electromagnetic spectrum,
also allow the integration of many different data types, duly
registered. The digital image processing can be divided in three different steps: pre-processing, enhancement, and classification. The pre-processing refers to the initial processing of the raw data for the radiometric calibration of the image, the correction of geometric distortions and the elimination of noise. The most common enhancement techniques in DIP are: contrast enhancement, filtering, arithmetic operations, IHS and principal components transformation. As to the classification techniques we have: supervised classification (by pixel), and unsupervised classification (by regions). NOTE: The user may choose not to use the classification algorithms, since he can decide to perform the direct interpretation of an enhanced image. As we will see the DIP techniques are always over the gray levels or digital numbers (DN) attributed to each pixel in the
image. Depending on the technique used the user will work with one
image only (one band or IL) or with many images, this last one known as
multispectral techniques, because it deals with many images of the same
region in different regions of the electromagnetic spectrum. See following about each of these techniques: Digital Image Processing Techniques
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