Unsupervised Change Detection in Hyperspectral Images using Feature Fusion Deep Convolutional Autoencoders

TL;DR

This paper introduces an unsupervised deep autoencoder approach with feature fusion for effective change detection in hyperspectral images, outperforming existing methods across multiple datasets.

Contribution

It proposes a novel feature fusion deep convolutional autoencoder for unsupervised change detection in hyperspectral images, enhancing feature extraction over existing methods.

Findings

Outperforms state-of-the-art methods in all tested datasets.

Effectively captures features across multiple receptive fields.

Operates without requiring labeled data.

Abstract

Binary change detection in bi-temporal co-registered hyperspectral images is a challenging task due to a large number of spectral bands present in the data. Researchers, therefore, try to handle it by reducing dimensions. The proposed work aims to build a novel feature extraction system using a feature fusion deep convolutional autoencoder for detecting changes between a pair of such bi-temporal co-registered hyperspectral images. The feature fusion considers features across successive levels and multiple receptive fields and therefore adds a competitive edge over the existing feature extraction methods. The change detection technique described is completely unsupervised and is much more elegant than other supervised or semi-supervised methods which require some amount of label information. Different methods have been applied to the extracted features to find the changes in the two images and it is found that the proposed method clearly outperformed the state of the art methods in unsupervised change detection for all the datasets.