# Multitask Deep Learning with Spectral Knowledge for Hyperspectral Image Classification

## TL;DR

This paper introduces a multitask deep learning approach that leverages spectral knowledge to improve hyperspectral image classification across multiple datasets, achieving higher accuracy and better generalization.

## Contribution

The paper presents a novel multitask deep learning framework that incorporates spectral knowledge to effectively utilize multiple datasets for hyperspectral image classification.

## Key findings

- Higher classification accuracy on three datasets
- Competitive results on the Salinas Valley dataset
- Spectral knowledge helps prevent overfitting

## Abstract

In this letter, we propose a multitask deep learning method for classification of multiple hyperspectral data in a single training. Deep learning models have achieved promising results on hyperspectral image classification, but their performance highly rely on sufficient labeled samples, which are scarce on hyperspectral images. However, samples from multiple data sets might be sufficient to train one deep learning model, thereby improving its performance. To do so, we trained an identical feature extractor for all data, and the extracted features were fed into corresponding Softmax classifiers. Spectral knowledge was introduced to ensure that the shared features were similar across domains. Four hyperspectral data sets were used in the experiments. We achieved higher classification accuracies on three data sets (Pavia University, Pavia Center, and Indian Pines) and competitive results on the Salinas Valley data compared with the baseline. Spectral knowledge was useful to prevent the deep network from overfitting when the data shared similar spectral response. The proposed method tested on two deep CNNs successfully shows its ability to utilize samples from multiple data sets and enhance networks' performance.