A Survey of Remote Sensing Image Classification Approaches

Abstract

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 remotesensing 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 knowledgebased 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.