Composite Clustering Sampling Strategy for Multiscale Spectral-Spatial Classification of Hyperspectral Images

In recent years, many high-performance spectral-spatial classification methods were proposed in the field of hyperspectral image classification. At present, a great quantity of studies has focused on developing methods to improve classification accuracy. However, some research has shown that the widely adopted pixel-based random sampling strategy is not suitable for spectral-spatial hyperspectral image classification algorithms. Therefore, a composite clustering sampling strategy is proposed, which can greatly reduce the overlap between the training set and the test set, while making sample points in the training set sufficiently representative in the spectral domain. At the same time, in order to solve problems of a three-dimensional Convolutional Neural Network which is commonly used in spectral-spatial hyperspectral image classification methods, such as long training time and large computing resource requirements, a multiscale spectral-spatial hyperspectral image classification model based on a two-dimensional Convolutional Neural Network is proposed, which effectively reduces the training time and computing resource requirements.