| Object Oriented Image Classification |
Article Index for Object |
Website Links For Object |
Information AboutObject Oriented Image Classification |
|
The object-oriented approach can speed up and increase the accuracy of a process that automatically extracts information from very high resolution aerial and satellite imagery like IKONOS and QuickBird . Automatic classification of digital images has been traditionally carried out Pixel -by-pixel. Pixel-by-pixel Algorithm s are slow when applied to high resolution images. An urban image is also better classified by the objects in it, rather than by pixels. Many urban land cover types, such as roads, buildings, parking lots, etc., are Spectrally similar. So, spatial information such as texture and context must be exploited to produce more accurate urban maps. Since these are inherently properties that cover several pixels, object-oriented classification can use them more easily than pixel-by-pixesl methods. Pixel-by-pixel methods are slower and less accurate when classifying images of urban environments, which consist of a mosaic of small-scale features made up of different materials. Digital images are usually classified by themes abstracted from the real world. The themes are supposed to aid particular application areas. With the right selection of themes, object-oriented image classification can be applied to studies of Geography , Military Intelligence , Ecology , Geology , and Marine Science . The only software currently available for object oriented classification is called eCognition. eCognition is a very powerful software by which it is possible to exploit the satellite images for the purpose of study. |
|
|