Skip to main content

Supervised land cover classification based on the locally reduced convex hull approach

Buy Article:

$60.90 plus tax (Refund Policy)

Abstract:

A novel supervised learning approach, called the locally reduced convex hull (LRCH), is proposed for land cover classification. The method described is capable of increasing the class separability and the representational capacity of the training set, which leads to its high generalization ability in applications. The effectiveness of the LRCH is demonstrated on the classification problem of a multi-spectral data set. In experiments, the LRCH was compared with six common classifiers. Statistical results in terms of the overall accuracy, the Kappa coefficient and McNemar's test show that LRCH outperforms most of the other approaches, with a speed that is comparable to all of them.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431161003636708

Affiliations: Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China

Publication date: March 1, 2010

More about this publication?
tandf/tres/2010/00000031/00000008/art00018
dcterms_title,dcterms_description,pub_keyword
6
5
20
40
5

Access Key

Free Content
Free content
New Content
New content
Open Access Content
Open access content
Subscribed Content
Subscribed content
Free Trial Content
Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more