Spectral-Spatial Classification of Hyperspectral Image Using Autoencoders

TL;DR

This paper introduces a novel deep learning framework using autoencoders for spectral-spatial feature extraction in hyperspectral image classification, significantly improving accuracy over classical methods.

Contribution

It is the first to integrate autoencoders with PCA for spectral-spatial feature extraction in hyperspectral image classification.

Findings

Achieves highest classification accuracy among tested methods.

Outperforms classical classifiers like SVM and PCA-based SVM.

Demonstrates effectiveness of deep autoencoders in hyperspectral data analysis.

Abstract

Hyperspectral image (HSI) classification is a hot topic in the remote sensing community. This paper proposes a new framework of spectral-spatial feature extraction for HSI classification, in which for the first time the concept of deep learning is introduced. Specifically, the model of autoencoder is exploited in our framework to extract various kinds of features. First we verify the eligibility of autoencoder by following classical spectral information based classification and use autoencoders with different depth to classify hyperspectral image. Further in the proposed framework, we combine PCA on spectral dimension and autoencoder on the other two spatial dimensions to extract spectral-spatial information for classification. The experimental results show that this framework achieves the highest classification accuracy among all methods, and outperforms classical classifiers such as SVM and PCA-based SVM.