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We Provide Information about Recognition methods in image processing and Interpretation method in Image Processing.
Recognition methods in image processing
Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance. Typical image recognition algorithms include:
Optical character recognition
Pattern and gradient matching
Face recognition
License plate matching
Scene change detection
Interpretation method in Image Processing
To derive useful spatial information from images is the task of image interpretation. It includes
- detection: such as search for hot spots in mechanical and electrical facilities and white spot in x-ray images. This procedure is often used as the first step of image interpretation.
- identification: recognition of certain target. A simple example is to identify vegetation types, soil types, rock types and water bodies. The higher the spatial/spectral resolution of an image, the more detail we can derive from the image.
- delineation: to outline the recognized target for mapping purposes. Identification and delineation combined together are used to map certain subjects. If the whole image is to be processed by these two procedures, we call it image classification.
- enumeration: to count certain phenomena from the image. This is done based on detection and identification. For example, in order to estimate household income of the population, we can count the number of various residential units.
- mensuration: to measure the area, the volume, the amount,and the length of certain target from an image. This often involves all the procedures mentioned above. Simple examples include measuring the length of a river and the acreage of a specific land-cover class. More complicated examples include an estimation of timber volume, river discharge, crop productivity, river basin radiation and evapotranspiration.
In order to do a good job in the image interpretation, and in later digital image analysis, one has to be familiar with the subject under investigation, the study area and the remote sensing system available to him. Usually, a combined team consisting of the subject specialists and the remote sensing image analysis specialists is required for a relatively large image interpretation task.
Depending on the facilities that an image interpreter has, he might interpret images in raw form, corrected form or enhanced form. Correction and enhancement are usually done digitally.
Elements on which image interpretation are based
- Image tone, grey level, or multispectral grey-level vector
Human eyes can differentiate over 1000 colors but only about 16 grey levels. Therefore, colour images are preferred in image interpretation. One difficulty involved is use of multispectral image with a dimensionality of over 3. In order to make use of all the information available in each band of image, one has to somehow reduce the image dimensionality.
Spatial variation of image tones. Texture is used as an important clue in image interpretation. It is very easy for human interpreters to include it in their mental process. Most texture patterns appear irregular on an image.
Regular arrangement of ground objects. Examples are residential area on an aerial photograph and mountains in regular arrangement on a satellite imagery.
A specific object co-occurring with another object. Some examples of association are an outdoor swimming pool associated with a recreation center and a playground associated with a school.
Object shadow is very useful when the phenomena under study have vertical variation. Examples include trees, high buildings, mountains, etc.
Agricultural fields and human-built structures have regular shapes. These can be used to identify various target.
Relative size of buildings can tell us about the type of land uses while relative sizes of tree crowns can tell us about the approximate age of trees.
Broad leaf trees are distributed at lower and warmer valleys while coniferous trees tend to be distributed on a higher elevation, such as tundra. Location is used in image interpretation.
Image interpretation strategies
Direct recognition: Identification of targets.
Land-cover classification
(Land cover is the physical evidence of the earth's surface.)
- indirect interpretation
to map something that is not directly observable in the image. This is used to classify land use types (Gong and Howarth, 1992b). Land-use is the human activities on a piece of land. It is closely related to land-cover types. For example, a residential land-use type is composed of roof cover, lawn, trees and paved surfaces.
- from known to unknown
To interpret an area where the interpreter is familiar with first, then interpret the areas where the interpreter is not familiar with (Chen et al, 1989). This can be assisted by field observation
- from direct to indirect
In order to obtain forest volume, one might have to determine what is observable from the image, such as tree canopies, shadows etc. Then the volume can be derived. We can also estimate the depth of permafrost using the surface cover information (Peddle, 1991).
- Use of collateral information
Census data,and topographical maps and other thematic maps may all be useful during image interpretation.
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Meenakshi Public School
