# Segmenting Hyperspectral Images Using Spectral-Spatial Convolutional Neural Networks With Training-Time Data Augmentation

## TL;DR

This paper introduces a spectral-spatial CNN for hyperspectral image classification that leverages data augmentation to improve accuracy and real-time performance despite limited training data.

## Contribution

The paper proposes a novel spectral-spatial CNN with training-time data augmentation, enhancing classification accuracy in hyperspectral imaging with limited ground-truth data.

## Key findings

- Outperforms existing spectral-spatial methods
- Achieves real-time hyperspectral classification
- Effective with limited training data

## Abstract

Hyperspectral imaging provides detailed information about the scanned objects, as it captures their spectral characteristics within a large number of wavelength bands. Classification of such data has become an active research topic due to its wide applicability in a variety of fields. Deep learning has established the state of the art in the area, and it constitutes the current research mainstream. In this letter, we introduce a new spectral-spatial convolutional neural network, benefitting from a battery of data augmentation techniques which help deal with a real-life problem of lacking ground-truth training data. Our rigorous experiments showed that the proposed method outperforms other spectral-spatial techniques from the literature, and delivers precise hyperspectral classification in real time.