Hyperspectral image is very useful for many computer vision tasks, however it is often difficult to obtain high-resolution hyperspectral images using existing hyperspectral imaging techniques. In this paper, we propose a deep residual convolutional neural network to increase the spatial resolution of hyperspectral image. Our network consists of 18 convolution layers and requires only one low-resolution hyperspectral image as input. The super-resolution is achieved by minimizing the difference between the estimated image and the ground truth high resolution image. Besides the mean square error between these two images, we introduce a loss function which calculates the angle between the estimated spectrum vector and the ground truth one to maintain the correctness of spectral reconstruction. In experiments on two public datasets we show that the proposed network delivers improved hyperspectral super-resolution result than several state-of-the-art methods.