Spain: A research conducted by Universidad Politécnica de Madrid’s School of Computing has developed new self-organising neural network training and visualisation algorithms for application in remote sensing (RS), generating simplified models of large volumes of multi-spectral information.
Neural networks are mathematical models inspired by the operation of biological neural networks. They are now applied across a wide range of disciplines to solve a broad spectrum of problems. One of the most widely used neural network models is what is known as the self-organising map.
The key problem related to remote sensing is the large volume of multi-dimensional data that has to be managed. The self-organising neural network, and specifically Kohonen’s model, has proved to be a versatile and useful tool for exploratory data analysis. But Kohonen’s model has some, primarily architecture-related, constraints. This has led to the emergence of new types of self-organising maps, like the Growing Cell Structures (GCS) model, that tackle these issues.
Using the GCS model, the relationships of the information input patterns can be visualised without the topological constraints of Kohonen’s model. On the downside, though, some training parameters are hard to configure, as, even if constant values are assigned, it is not clear what the permitted value range for these patterns is.
By proposing a new GCS model training algorithm that improves this network-input space topology fit, the research developed at the School of Computing comes closer to solving this problem. The modification of the GCS algorithm makes this neural network model easier to use to generate simplified models of the large volumes of multi-spectral information typically associated with the remote sensing field.
With the aim of exploiting this paradigm within the remote sensing field, the research project has developed several GCS-based multidimensional information visualisation methods, as well as a number of network labelling techniques for semi-supervised and unsupervised classification or multispectral information-based variable estimation processes. Additionally, as part of the research, several GCS-adapted measures have been developed to evaluate the quality of the trained network.
The developed methodology has been applied across a range of hot topics in the remote sensing field, like classification of land covers in semi-supervised and supervised processes, evaluation of the quality of training areas selection, estimation of the physical variables of aqueous covers or the analysis of spectral index validity for images with special features.
Source: The Medical News