Spatially weighted supervised classification for remote sensing


Atkinson, P.M. (2004) Spatially weighted supervised classification for remote sensing International Journal of Applied Earth Observation and Geoinformation, 5, (4), pp. 277-291. (doi:10.1016/j.jag.2004.07.006).

Download

[img] PDF Atkinson_JAG_2004.pdf - Other
Restricted to Registered users only

Download (527kB)

Description/Abstract

A simple approach for incorporating a spatial weighting into a supervised classifier for remote sensing applications is presented. The classifier modifies the feature-space distance-based metric with a spatial weighting. This is facilitated by the use of a non-parametric (k-nearest neighbour, k-NN) classifier in which the spatial location of each pixel in the training data set is known and available for analysis. A remotely sensed image was simulated using a combined Boolean and geostatistical unconditional simulation approach. This simulated image comprised four wavebands and represented three classes: Managed Grassland, Woodland and Rough Grassland. This image was then used to evaluate the spatially weighted classifier. The latter resulted in modest increase in the accuracy of classification over the original k-NN approach. Two spatial distance metrics were evaluated: the non-centred covariance and a simple inverse distance weighting. The inverse distance weighting resulted in the greatest increase in accuracy in this case.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1016/j.jag.2004.07.006
ISSNs: 0303-2434 (print)
Keywords: k-NN approach, remote sensing, spatially weighted
Subjects:
ePrint ID: 15770
Date :
Date Event
2004Published
Date Deposited: 01 Jun 2005
Last Modified: 16 Apr 2017 23:27
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/15770

Actions (login required)

View Item View Item