Integrating local and global error statistics for multi-scale RBF network training: an assessment on remote sensing data.

ABSTRACT:

Background

This study discusses the theoretical underpinnings of a novel multi-scale radial basis function (MSRBF) neural network along with its application to classification and regression tasks in remote sensing. The novelty of the proposed MSRBF network relies on the integration of both local and global error statistics in the node selection process.

Methodology and principal findings

The method was tested on a binary classification task, detection of impervious surfaces using a Landsat satellite image, and a regression problem, simulation of waveform LiDAR data. In the classification scenario, results indicate that the MSRBF is superior to existing radial basis function and back propagation neural networks in terms of obtained classification accuracy and training-testing

SUBMITTER: Mountrakis G 

PROVIDER: S-EPMC3411665 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature