By Topic

Integration of domain knowledge in the form of ancillary map data into supervised classification of remotely sensed data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Friedl, M.A. ; Dept. of Geogr., Boston Univ., MA, USA ; McIver, D.K. ; Brodley, C.E.

In recent years, machine learning and data mining methods have become increasingly common in remote sensing applications. One area in which such techniques are particularly useful is classification of remotely sensed data for land cover and vegetation mapping applications. In this paper, we describe new methods to include available information (domain knowledge) in supervised classification of land cover using high dimensional remote sensing observations. specifically, land cover and vegetation classification schemes are generally designed for ecological or land use applications. As a result, the classes of interest are often poorly separable in the multi-spectral or multi-temporal feature space provided by remote sensing. In many cases, ancillary data sources can provide useful information to help distinguish between problematic classes. However, available methods for including ancillary data sources, such as the use of prior probabilities in maximum likelihood classification, are often problematic in practice. This paper presents a method for incorporating prior probabilities in remote sensing-based land cover classification using a supervised decision tree classification algorithm. The method exploits recent theory from the domain of statistics and machine learning that allows robust estimates of class membership to be estimated using a technique known as boosting. This approach allows poorly separable classes to be distinguished based on ancillary information, but does not penalize rare classes.

Published in:

Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International  (Volume:2 )

Date of Conference: