Due to the massive parameters and complex structure

deep learning networks are usually trained in a long time with large-scale training samples. In this paper

we propose a spatial-spectral convolutional dense network (SSCDenseNet) which mainly targets limited samples for hyperspectral image classification. Three novel strategies are proposed to construct the proposed network. First

a spatial-spectral separable convolution method is adopted to make up a hidden layer unit with a spectral one-dimensional convolutional layer and a spatial two-dimensional convolutional layer; then the deep network is constructed by stacking multiple units. Second

we use batch normalization before each hidden layer unit to reduce covariance drift of data and accelerate the network training procedure. Finally

a direct connection between every two units is adopted to reuse hierarchical features

and solve the problem of gradient vanishing. The comprehensive evaluation of experiments on different datasets such as Indian Pines

Pavia University and Salinas are conducted to show the performance of the SSCDenseNet

and the results show that the proposed method outperforms several state-of-the-art deep learning based methods in terms of classification performance

especially under small-scale samples.