Object classification, segmentation, and parameter estimation in multichannel images by classifier learning with clustering of local parameters

Abstract

In different applications, it is often desirable to retrieve useful information from multichannel (color, multispectral, dual or full-polarization) images. On one hand, multichannel images are potentially able to provide a lot of useful information about sensed objects (terrains). On the other hand, the task of its reliable extraction is very complicated. And there are many reasons behind this like inherent noise, lack of a priori information about object features, complexity of scenes, etc. Therefore, numerous different approaches based on various functional principles and mathematical background have been already put forward. In majority of them, image classification and segmentation are common operations that precede estimation of object parameters. However, practically all methods are far away from completeness and/or perfection since they suffer from different drawbacks and application restrictions. Recently we have proposed methods based on learning with local parameter clustering that were rather successfully applied to image locally adaptive filtering and detection of objects with certain properties. This paper is an attempt to extend this approach to image classification, segmentation and object parameter estimation. A particular application of substance quantitative analysis from color images is considered. The proposed approach is shown to solve the aforementioned task quite well and to have a rather high potential for other applications.