A knowledge-integrated stepwise optimization model for feature mining in remotely sensed images

J. C. Luo, J. Zheng, Y. Leung, C. H. Zhou
2003 International Journal of Remote Sensing  
The selection of features, including spectral, texture, shape, size, and signal strength, is an important step in computerized information analysis of remotely sensed images. A feature space, which can be generally understood as a multidimensional space consisting of multiple individual features, can be modelled by estimating the distribution of the whole space with prior assumed probability distribution functions (PDFs) once only. However, due to the interoverlapping phenomenon among points or
more » ... the confusing influence from surrounding discrete points, it is very difficult to obtain the subtle and procedural structure of the mixture distributions of feature space, and hence to influence accuracy and interpretability of the results in the course of analysis. ; Extending on the method of Gaussian mixture modeling and decomposition (GMDD), a new feature mining method-stepwise optimization model (SOM) with genetic algorithms (GA) was proposed in this study for the extraction of tree-like hierarchical structure of unknown feature distributions in a feature space. To approximate reality accurately, integration of SOM-GA with symbolic geographical knowledge is essential in the feature mining and classification of remotely sensed images. Knowledge-integrated SOM-GA model that combines the power of SOM-GA and logic reasoning of rule-based inference was therefore proposed. The paper presents conceptual and technical discussions of the model in detail, along with the result of practical application test on a district in Hong Kong region.
doi:10.1080/0143116031000114833 fatcat:ze2ifok2bbg4lfubfjjnjl4qey