  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.
