Efficient Processing and Analysis of Images Using Neural Networks

A neural network based approach for efficient processing and analysis of images in remote sensing applications is proposed in this paper. Classification, segmentation, coding and interpretation of e.g., Landsat image data are tasks which can take advantage of this approach. First, we use multi-resolution analysis of the images in order to obtain image representations of lower sizes, to which neural networks can be applied more effectively. It is shown that auto-associative neural networks can be used to perform the multi-resolution analysis in an optimal way. Hierarchical neural networks are designed which are able to implement the analysis and classification task at different resolutions. Neural networks are also proposed as an efficient means for selecting regions of interest in the images, further reducing the image representation size which is necessary for effective analysis and classification of the images. Application of the proposed approach to specific real life remote sensing image data is currently under investigation.