# Deep Neural Network Based Hyperspectral Pixel Classification With Factorized Spectral-Spatial Feature Representation

## TL;DR

This paper introduces a novel deep neural network that leverages spectral-spatial factorization for hyperspectral pixel classification, achieving superior accuracy with fewer parameters compared to existing methods.

## Contribution

A new neural network architecture that efficiently exploits spectral-spatial features and reduces model complexity for hyperspectral data classification.

## Key findings

- Outperforms state-of-the-art methods in accuracy.
- Uses fewer parameters and smaller network size.
- Effective spectral-spatial feature integration.

## Abstract

Deep learning has been widely used for hyperspectral pixel classification due to its ability of generating deep feature representation. However, how to construct an efficient and powerful network suitable for hyperspectral data is still under exploration. In this paper, a novel neural network model is designed for taking full advantage of the spectral-spatial structure of hyperspectral data. Firstly, we extract pixel-based intrinsic features from rich yet redundant spectral bands by a subnetwork with supervised pre-training scheme. Secondly, in order to utilize the local spatial correlation among pixels, we share the previous subnetwork as a spectral feature extractor for each pixel in a patch of image, after which the spectral features of all pixels in a patch are combined and feeded into the subsequent classification subnetwork. Finally, the whole network is further fine-tuned to improve its classification performance. Specially, the spectral-spatial factorization scheme is applied in our model architecture, making the network size and the number of parameters great less than the existing spectral-spatial deep networks for hyperspectral image classification. Experiments on the hyperspectral data sets show that, compared with some state-of-art deep learning methods, our method achieves better classification results while having smaller network size and less parameters.