Extracting the Rice Planting Areas Using an Artificial Neural Network
利用神经网络方法提取水稻种植面积—以湖北省双季早稻为例

Rice is one of the main grain crops in our country. The rice planting area and yield could affect argicultural development and economic stability in China. For NOAA has a lower spatial resolution, the result is that most AVHRR pixels contain a mixture of land cover classes which influence the accuracy of classification. Methods for unmixing the mixed pixels have been used in a range of studies and the accuracy of the analyses has often increased. However, there are many problems in these methods. For instance, because sometimes the same objects have different spectra, or the same spectra represents different objects, so identifying the land cover classes successfully is very difficult by only relying on spectra data and obtaining end member spectra is another problem. In this paper, an alternative approach, the artificial neural network which makes no assumption about the nature of the mixing and does not require end member spectra, is presented. We select five factors (NDVI, temperature difference, soil type, landuse type and DEM), which have important effect on the distribution of rice planting, as the nodes of input layer to classify the image. The planting areas of early rice in Hubei province are obtained.