Deep Feature Aggregation Network for Hyperspectral Remote Sensing Image Classification

Hyperspectral remote sensing images (HSIs) are rich in spectral–spatial information. The deep learning models can help to automatically extract and discover this rich information from HSIs for classifying HSIs. However, the sampling of the models and the design of the hyperparameters depend on the number of samples and the size of each sample's input space. In the case of limited samples, the description dimension of features is also limited and overfitting to other remote sensing image datasets is evident. This study proposes a novel deep feature aggregation network for HSI classification based on a 3-D convolutional neural network from the perspective of feature aggregation patterns. By introducing the residual learning and dense connectivity strategies, we established a deep feature residual network (DFRN) and a deep feature dense network (DFDN) to exploit the low-, middle-, and high-level features in HSIs. For the Indian Pines and Kennedy Space Center datasets, the DFRN model was determined to be more accurate. On the Pavia University dataset, both the DFDN and DFRN have basically the same accuracy, but the DFDN has faster convergence speed and more stable performance on the validation set than the DFRN. Therefore, when faced with different HSI data, the corresponding aggregation method can be chosen more flexibly according to the requirements on number of training samples and the convergence speed. This is beneficial in the HSI classification.