Effects of various feature information on the accuracy of winter wheat planting area measurement

It is always an important piece of work to fully mine remote sensing image information and improve identification environment in agricultural crops remote sensing monitoring. Ancient study indicates optimal bands combination, texture and vegetation indices can advance classification accuracy in a certain extent. However, whether they can contribute to improve the crop identification accuracy out of question and whether they have identical response to different classifiers. These above problems, which are very important and valuable in agricultural crops area monitoring, are currently less researched. Hence, in this paper, seven types of common texture and five vegetation indices were respectively added into TM multispectral bands to classify using three different methods, which are Minimum Distance, Maximum Likelihood and Support Vector Machine, and analyze the effect on winter wheat identification accuracy by comparing the classification results. The contexture include Mean, Variance, Homogeneity, Contrast, Dissimilarity, Entropy, Variance, Angular Second Moment and Correlation, and the vegetation indices are RVI, SAVI, RDVI, NDWI and SLAVI. Results show that the optimal bands combination(band 5th, 4th and 3th), contexture and vegetation indices do not contribute to advance the wheat area measurement accuracy in this area. Same feature information combinations have diverse response to different classifiers. So, the interpreters should not blindly use feature information in wheat planting area measurement. How to choose the appropriate feature information is related to not only study area characteristics but also classifier.