International Journal of Applied Mathematics and Machine LearningAuthor's: Li Li, Jianqiang Gao and Balan Sethuramalingam
Pages: [1] - [11]
Received Date: July 01, 2015
Submitted by:
DOI: http://dx.doi.org/10.18642/ijamml_7100121516
Remote-sensing image classification is a complex process that may be affected via lots of factors. This paper mainly examines current practices, problems, and prospects of remote-sensing image classification. The aim is to focus on the summarization of major advanced classification methods and the techniques used for improving classification accuracy. This paper review suggests that designing a suitable image processing procedure is a prerequisite for a successful classification of remotely sensed data and the selection of a suitable classification accuracy. Neural network, decision tree classifier, and knowledge-based classification have increasingly become important methods for remote-sensing image classification. However, more research is needed to identify and reduce uncertainties in the image processing chain to improve classification accuracy.
remote-sensing image, classification approach, preprocessing of image, feature extraction and selection.
