Showing posts with label What is Image Classification?. Show all posts
Showing posts with label What is Image Classification?. Show all posts

Friday 18 April 2014

GIS Application

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GIS Application
GIS is a technique or an information system that provides spatial information within and outside the planet earth. It is one of the best ways to explore as it gives the integrate information about the attributes in every field. It mainly gives information about the geographical data through map that are also in digital form.

To make a better use of GIS it in important to know about the application and the use of GIS in its field like business, transport, tourism etc.

There are 5 major applications used in diverse field which are as follows:
 
1. Facilities management
2. Natural resource management
3. Transport network
4. Planning and engineering
5. Land information system
      i)  Business
      ii) Agriculture
 
1. Facilities management- Large scale and precise maps and network analysis are used mainly for utility management. Automated mapping/facility management is frequently used in this area.

2. Natural resource management- It is an overlay technique and maps with scales of medium and small that shows the combination of aerial photographs and satellite images which are used for natural resource management.

3. Transport network- Maps of large or medium scale and spatial analysis are used for the purpose of networking,location of streets etc.

4. Planning and Engineering- Engineering models along with large and medium scale maps are mainly used in civil engineering.

5. Land information system- Large scale Cadastre maps or land parcel maps and spatial analysis are used for Cadastre administration, taxation etc.

     i)  Business- GIS is a tool that helps in locating things and managing the business through tracing the business sites and customers.

     ii)  Agriculture- GIS application is used in agricultural field that shows the yield of crops,species,region,soil etc.

Other than 5 major applications along with agriculture and business application some more important applications are environment, geology, hydrology, mapping, military, forestry, etc. Therefore, there are numerous aspects and advantages to gather new and important information through GIS in diverse field to make a better decision.  

Monday 14 April 2014

What is Image Classification?

What is Image Classification?
What is Image Classification?

Remote sensing is the science and the art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not contact with the object, area or phenomenon under investigation.(Lillesand and Kiefer, 1994). The images taken are in the form of pixel and the process of changing it into digital images that make sense is known as image classification.It is based on technique that provides information through images. For eg.Land cover futher categorised into- forest,water,agriculture etc.

The two basic classifications are:

  1. Supervised
  2. Unsupervised

1.  Supervised- This classification requires "training sites" where a person is aware about the ground so that a polygon can be digitized of that area.The image processing software system is then used to develop a statistical characterization of the reflectance for each information class. This stage is often called "signature analysis" and may involve developing a characterization as simple as the mean or the rage of reflectance on each bands, or as complex as detailed analyses of the mean, variances and covariance over all bands.(Eastman, 1995)

Supervised classification is further classified into:
  • Parallelpiped
  • Minimum distance to mean
  • Maximum likelihood

2.  Unsupervised- Unsupervised classification is a method which examines a large number of unknown pixels and divides into a number of classed based on natural groupings present in the image values. unlike supervised classification, unsupervised classification does not require analyst-specified training data. The basic premise is that values within a given cover type should be close together in the measurement space (i.e. have similar gray levels), whereas data in different classes should be comparatively well separated (i.e. have very different gray levels) (PCI, 1997; Lillesand and Kiefer, 1994; Eastman, 1995 )

Unsupervised is further classified into technique:

  • K-Means classification
  • SODATA classification
  • Expectation Maximization (EM)classification

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