# Segmentation-Aware Hyperspectral Image Classification

## TL;DR

This paper introduces a unified hyperspectral image classification approach combining deep neural networks with segmentation-aware superpixels, achieving state-of-the-art results and improving performance with limited training data.

## Contribution

The paper presents a novel integration of segmentation-aware superpixels with deep residual networks for hyperspectral image classification.

## Key findings

- Achieves state-of-the-art results on benchmark datasets.
- Segmentation-aware superpixels significantly improve classification with limited training data.
- The method effectively combines spectral and spatial information.

## Abstract

In this paper, we propose an unified hyperspectral image classification method which takes three-dimensional hyperspectral data cube as an input and produces a classification map. In the proposed method, a deep neural network which uses spectral and spatial information together with residual connections, and pixel affinity network based segmentation-aware superpixels are used together. In the architecture, segmentation-aware superpixels run on the initial classification map of deep residual network, and apply majority voting on obtained results. Experimental results show that our propoped method yields state-of-the-art results in two benchmark datasets. Moreover, we also show that the segmentation-aware superpixels have great contribution to the success of hyperspectral image classification methods in cases where training data is insufficient.