The question of whether there is a preferred or best classifier to use with remotely sensed data is discussed, focussing on likely results and ease of training. By appealing in part to the No Free Lunch Theorem, it is suggested that there is really no superiority of one well trained algorithm over another, but rather it is the means by which the algorithm is employed - ie. the classification methodology - that often governs the outcomes.
GIS data may include optical and radar imagery, and categorical information such as soils maps and land planning strategies, all of which can assist thematic mapping. We now have several decades of experience with thematic mapping from spectral data alone. We also have experience with the analysis of radar imagery, while hyperspectral thematic mapping techniques are now also becoming feasible. However, successful machine-assisted analysis of mixed optical and radar data is not straightforward, and is complicated further when categorical data is also involved. Often simplistic methods involving stacked vectors of all the available data are used, but the incommensurate data types means that a single analytical procedure, even if acceptable, will often yield poor results. Methods commonly used for mixed image data analysis are reviewed and a set of desirable criteria for an operational method for thematic mapping from disparate data types are presented. Finally we propose a fusion strategy based on (i) analysing each data type with procedures most suited to its particular characteristics and (ii) fusing at the class level, involving combination rules that work with labels rather than measurement vectors. The method is proposed as suited to GIS analysis, particularly when the data sets are distributed and thus accessed over a network.
Conference Committee Involvement (15)
Image and Signal Processing for Remote Sensing
11 September 2017 | Warsaw, Poland
Image and Signal Processing for Remote Sensing
26 September 2016 | Edinburgh, United Kingdom
Image and Signal Processing for Remote Sensing
21 September 2015 | Toulouse, France
Image and Signal Processing for Remote Sensing
22 September 2014 | Amsterdam, Netherlands
Image and Signal Processing for Remote Sensing XIX
23 September 2013 | Dresden, Germany
Image and Signal Processing for Remote Sensing
24 September 2012 | Edinburgh, United Kingdom
Image and Signal Processing for Remote Sensing
19 September 2011 | Prague, Czech Republic
Image and Signal Processing for Remote Sensing
20 September 2010 | Toulouse, France
Image and Signal Processing for Remote Sensing
31 August 2009 | Berlin, Germany
Image and Signal Processing for Remote Sensing
15 September 2008 | Cardiff, Wales, United Kingdom
Image and Signal Processing for Remote Sensing
18 September 2007 | Florence, Italy
Image and Signal Processing for Remote Sensing XII
13 September 2006 | Stockholm, Sweden
Image and Signal Processing for Remote Sensing XI
20 September 2005 | Bruges, Belgium
Image and Signal Processing for Remote Sensing X
13 September 2004 | Maspalomas, Canary Islands, Spain
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
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.