  Hyperspectral image (HSI) classification is widely used for the analysis of
remotely sensed images. Hyperspectral imagery includes varying bands of images.
Convolutional Neural Network (CNN) is one of the most frequently used deep
learning based methods for visual data processing. The use of CNN for HSI
classification is also visible in recent works. These approaches are mostly
based on 2D CNN. Whereas, the HSI classification performance is highly
dependent on both spatial and spectral information. Very few methods have
utilized the 3D CNN because of increased computational complexity. This letter
proposes a Hybrid Spectral Convolutional Neural Network (HybridSN) for HSI
classification. Basically, the HybridSN is a spectral-spatial 3D-CNN followed
by spatial 2D-CNN. The 3D-CNN facilitates the joint spatial-spectral feature
representation from a stack of spectral bands. The 2D-CNN on top of the 3D-CNN
further learns more abstract level spatial representation. Moreover, the use of
hybrid CNNs reduces the complexity of the model compared to 3D-CNN alone. To
test the performance of this hybrid approach, very rigorous HSI classification
experiments are performed over Indian Pines, Pavia University and Salinas Scene
remote sensing datasets. The results are compared with the state-of-the-art
hand-crafted as well as end-to-end deep learning based methods. A very
satisfactory performance is obtained using the proposed HybridSN for HSI
classification. The source code can be found at
\url{https://github.com/gokriznastic/HybridSN}.
