A survey on RSI classification techniques
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Abstract
Remote sensing image (RSI) classification is an important content of RS research area in geological survey, mineral exploration, geological evaluation and disaster monitoring and basic geological research. RSI classification, which is a complex process that may be affected by many factors, used to classify different features available in the image. The present paper investigates the process of the RSI classification, current practices, limitations, and its future. The main emphasis is given to summarize the major classification algorithms, approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are also discussed. This investigation suggests that developing computationally efficient algorithms for image classification without compromising the classification accuracy is of primary importance. Effective use of multiple features of remotely sensed data and the selection of suitable RSI classification algorithms are especially significant for enhancing classification accuracy. In the existing algorithms such as Parallelepiped, minimum distance to mean, maximum likelihood, nearest neighborhood, k-mean, and ISODATA, etc. cited in this paper, all pixel data of image are used as feature vector for extracting the information from image, but the threshold values of pixels based on BTC, can be used for better performance. Integration of remote sensing with geographical information systems (GIS) and expert classification system is emerging as an appealing research direction.
Keywords Remote Sensing Image (RSI), geographical information systems (GIS), efficient algorithms, classification accuracy, etc.
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