# Deep Learning Hyperspectral Image Classification Using Multiple Class-based Denoising Autoencoders, Mixed Pixel Training Augmentation, and Morphological Operations

arXiv: https://arxiv.org/abs/1807.10574 · HTML: https://mlsanity.com/paper/1807.10574/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 3 of 3)

- [The Effects of Spectral Dimensionality Reduction on Hyperspectral Pixel Classification: A Case Study](https://mlsanity.com/paper/2104.00788/) (2021)
- [Morphological Operation Residual Blocks: Enhancing 3D Morphological Feature Representation in Convolutional Neural Networks for Semantic Segmentation of Medical Images](https://mlsanity.com/paper/2103.04026/) (2021)
- [UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation](https://mlsanity.com/paper/2406.16129/) (2024)

## Abstract (arXiv)

Herein, we present a system for hyperspectral image segmentation that utilizes multiple class--based denoising autoencoders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system with edge pixels which are almost always mixed pixels. Finally, we utilize a deep neural network and morphological hole-filling to provide robust image classification. Results run on the Salinas dataset verify the high performance of the proposed algorithm.