Using neural networks to map Africa's land cover with Landsat ETM+ SLC-off imagery

M. J. Aitkenhead

doi:10.1117/12.829028

18 September 2009 Using neural networks to map Africa's land cover with Landsat ETM+ SLC-off imagery

M. J. Aitkenhead

Proceedings Volume 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI; 74720B (2009) https://doi.org/10.1117/12.829028
Event: SPIE Remote Sensing, 2009, Berlin, Germany

Abstract

Landsat SLC-off imagery has been downloaded for the whole of Africa, with the imagery being acquired by the satellite during the period September 2007 to May 2008. This imagery is intended to be used for the production of a land cover map of Africa at Landsat ETM+ image resolution. The quantity of data (>1000 scenes, each containing ~40 million pixels) means that automated image analysis is required in order to achieve this. Manual identification of land cover classes has been carried out and a classification system developed that is based on the FAO's LCCS. This classification system is designed to provide a level of detail that will be useful to land managers, farmers and environmentalists alike. Prior to mapping the entire continent it is necessary to determine whether or not the method selected will produce sufficiently accurate maps. Here a test of the classification method, which uses neural network classification followed by nearest-neighbour interpolation, is described. Results show that the mapping accuracy is greater than 85% for all classes where the pixels were available, and that interpolation of missing pixels post-classification gives accuracy greater than 80% for all classes (the number of classes varied between scenes, but was generally between 5 and 10). In addition, key requirements for the development of a continental land cover map of Africa have been identified during this work.

Citation Download Citation

M. J. Aitkenhead "Using neural networks to map Africa's land cover with Landsat ETM+ SLC-off imagery", Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 74720B (18 September 2009); https://doi.org/10.1117/12.829028

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Earth observing sensors

Landsat

Neural networks

Classification systems

Remote sensing

Associative arrays

Water

Show All Keywords

Keywords/Phrases

Search In:

Publication Years