Change detection for mapping paddy using support vector data description

Remote sensing provides a way to identify paddy and measure the planting area with high efficiency. Particularly, the multi-temporal images show dominant advantages of extracting short-term spectral features of paddy. Traditional change detection methods usually try to derive exhaustive training dataset for classification which is not suitable for those applications that are interested on one specific class. This work introduces a method for paddy mapping by Support vector data description (SVDD) with multi-temporal HJ-1 images, at a typical study area in the middle region of Jiangsu Province. This method aims at mapping the image data into a high dimensional feature space where a hypersphere that encloses most of the patterns belonging to the class of interest (target class) and rejecting the rest (outliers) can be defined. The results showed that the proposed method could precisely identify the characteristic spectral change of paddy and efficiently map paddy with, the overall accuracy at 93.75%, which is slightly lower than that with SVM approach 95.25%. This method decreases the difficulties in training data and could be adapted for further applications.