  The Hyperspectral image (HSI) classification is a standard remote sensing
task, in which each image pixel is given a label indicating the physical
land-cover on the earth's surface. The achievements of image semantic
segmentation and deep learning approaches on ordinary images have accelerated
the research on hyperspectral image classification. Moreover, the utilization
of both the spectral and spatial cues in hyperspectral images has shown
improved classification accuracy in hyperspectral image classification. The use
of only 3D Convolutional Neural Networks (3D-CNN) to extract both spatial and
spectral cues from Hyperspectral images results in an explosion of parameters
hence high computational cost. We propose network architecture called the
MixedSN that utilizes the 3D convolutions to modeling spectral-spatial
information in the early layers of the architecture and the 2D convolutions at
the top layers which majorly deal with semantic abstraction. We constrain our
architecture to ResNeXt block because of their performance and simplicity. Our
model drastically reduced the number of parameters and achieved comparable
classification performance with state-of-the-art methods on Indian Pine (IP)
scene dataset, Pavia University scene (PU) dataset, Salinas (SA) Scene dataset,
and Botswana (BW) dataset.

Computer Vision and Pattern Recognition;Machine Learning;Image and Video Processing