Classification_Notes - Geog 182 Classification notes...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon
1 Geog 182 Classification notes Classification is the most popularly used information extraction techniques in digital remote sensing. A classification unit is defined as the image segment on which a classification decision is based. A classification unit could be a pixel, a group of neighbouring pixels or the whole image. Conventional multispectral classification techniques perform class assignments based only on the spectral signatures of a classification unit. Contextual classification refers to the use of spatial, temporal, and other related information, in addition to the spectral information of a classification unit in the classification of an image. Usually, it is the pixel that is used as the classification unit. General image classification procedures include: (1) Design image classification scheme: they are usually information classes such as urban, agriculture, forest areas, etc. Conduct field studies and collect ground infomation and other ancillary data of the study area. (2) Preprocessing of the image, including radiometric, atmospheric, geometric and topographic corrections, image enhancement, and initial image clustering. (3) Select representative areas on the image and analyze the initial clustering results or generate training signatures. (4) Image classification Supervised mode: using training signature Unsupervised mode: image clustering and cluster grouping (5) Post-processing: complete geometric correction & filtering and classification decorating. (6) Accuracy assessment: compare classification results with field studies.
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2 The following diagram shows the major steps in two types of image classification: Supervised: Unsupervised In order to illustrate the differences between the supervised and unsupervised classification, we will introduce two concepts: information class and spectral class: Information class : a class specified by an image analyst. It refers to the information to be extracted. Spectral class : a class which includes similar grey-level vectors in the multispectral space. In an ideal information extraction task, we can directly associate a spectral class in the multispectral space with an information class. For example, we have in a two dimensional space three classes: water, vegetation, and concrete surface.
Background image of page 2
3 By defining boundaries among the three groups of grey-level vectors in the two-dimensional space, we can separate the three classes. One of the differences between a supervised classification and an unsupervised one is the ways of associating each spectral class to an information class. For supervised classification, we first start with specifying an information class on the image. An algorithm is then used to summarize multispectral information from the specified areas on the image to form class signatures. This process is called supervised training. For the unsupervised case,however, an algorithm is first applied to the image and some spectral classes (also called clusters) are formed. The image analyst then try to assign a spectral class to the desirable
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 4
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 09/08/2010 for the course GEOG 182 at San Jose State University .

Page1 / 20

Classification_Notes - Geog 182 Classification notes...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online