Detailed mapping of a salt farm from Landsat TM imagery using neural network and maximum likelihood classifiers: a comparison

Abstract

Information on the size and distribution of various zones in a salt farm is critical to salt farm management and estimation of salt yield. The ability of neural network and maximum likelihood classifiers to classify spectrally uniform water bodies with a distinct boundary in a salt farm is comparatively studied in this paper for the Taibei Salt Field, Jiangsu Province, East China using Landsat Thematic Mapper (TM) data. In a pre‐run classification of general land covers, the salt farm was mapped 84% correctly using the neural network method, slightly higher than the 76% achieved with the maximum likelihood classifier. In another separate neural network classification the salt farm was mapped further into three zones of evaporation, condensation, and crystallization at a producer's accuracy of 76%, 84%, and 86%, respectively, with the optimum classification settings. Such a detailed classification was not possible with the maximum likelihood method. It is concluded that the neural network is superior to the maximum likelihood method for detailed mapping of the Taibei Salt Field where salty water bodies are spectrally uniform and spatially extensive on the image with clear‐cut boundaries among them.

Acknowledgments

Funding for this study was received from the joint key project of National Natural Science Foundation of China (no. 50339010), and State ‘211’ Key Project: Environmental Change and Ecological Construction on Multi‐Spatiotemporal Scales. Two anonymous referees provided valuable comments on an earlier version of this paper.