Polygonal Partition-Based Hyperspectral Image Classification with Single Labeled Sample

It is well known that classification accuracy highly relies on the number of labeled samples. However, it is difficult to obtain sufficient labeled samples in real-world applications. To solve this issue, a novel hyperspectral image (HSI) classification method based on polygonal partition is proposed for crop mapping. This method only needs single sample per class as an initial training set. Specifically, multiscale polygonal partition is applied on the first three components of the HSI. Then, a spectral similarity-based sample expansion method is proposed to obtain more labeled samples. Next, a pixel-wise classifier, the support vector machine (SVM), is used to acquire an initial classification result. Finally, classification result is further optimized according to the partition maps. Experimental results show that classification performance of the proposed method is satisfactory even when the number of labeled sample is single for each class.