Study of Various Image Fusion Approaches for Extraction and Classification of Infrastructural Growth

Abstract

Estimation of infrastructural growth is the key issue for planning and resource management. In this regard it is highly required to have a proper database and documentation. Remotely sensed data and its processing techniques are most important parameter to achieve this goal. In developing countries, the planning and resource management is still dependent on traditional methods, but integration of satellite data of high resolution and of multiple spectral bands with appropriate processing techniques, makes it possible to get optimal result in limited fiscal resources. The merging of multi resolution sensor data can be the best option instead of using costly data for low budget planning and development. This study aims to analyze the potentials of image fusion of multispectral and panchromatic satellite data with high ground resolution images and evaluating their significance in infrastructural classification. While the accuracy assessment tests of classification result, suggest the appropriate classification techniques. The Relevance of image fusion in auto vectorization has also been discussed in this paper.