Hyperspectral image classification by parameters prediction networks

Hyperspectral image, which contains high-resolution spectral information as well as large-scale spatial information, has been widely used in various classification applications of remote sensing area. However, due to the insufficient of the labeled samples in the training set and the unbalance of sample quantity between different classes, traditional supervised classification methods are difficult to achieve satisfying performance. In order to address above issues, this paper studies on how to predict classification parameters more effectively, and finds out that the parameters of the fully-connected layer in the classifier are closely related to the output of the feature mapping layer. Based on above fact, this paper proposes a hyperspectral image classification method base on parameter prediction network, which adapts a pre-trained neural network to novel categories by directly predicting the parameters of classifier from the feature data of the hyperspectral image. Experimental results and analysis demonstrate the competitive performance of the proposed method over other state-of-the-art classification methods based on neural network when the number of labeled samples is very small.