Galaxy catalogues with large number of images of galaxies observed at
several wavelengths have become available. Examples of such catalogues
are produced by survyes such as SDSS, DES, and CANDELS.
Using these catalogues, galaxies are classified utilizing eye-fit such as
the one applied to Galaxy Zoo project. Such an excercise is very time consuming
and it is not apt for big datasets. In addition, recent catalogues include galaxies
at higher redshift. These galaxies are observed with poor resolution and
the galaxy evolution with redshifts may mean that the traditional galaxy
classification scheme may not be sufficient.
Other automated methods that are applied to large datasets,
either extract morphological parameters such as concentration, clumpiness,
asymmetry etc., or estimate photometric parameters.

Recent times Machine Learing techniques such as Convolutional Neural Networks (CNN)
and Support Vector Machine (SVM) are applied to galaxy classification problem with
varying degree of success. Many authors have applied CNN based
schemes along with many techniques to improve the success rate, such as
dropout regularization and transfer learning.
However these techniques require large computational power and human created
training catalogues. The resulting output classes are restricted to the classes
in the training dataset.

Recently the technique of "Topological Data Analysis (TDA)" is being used for image
segmentation, classification and object detection. This method uses techniques
like persistent homology or tSNE to identify topologically connected components
in the images. Although this technique is used in other sciences,
it has not been used much in astronomy. The TDA technique is applied to studying
large scale structures, such as finding voids and filaments and
Cosmic Microwave Background (CMB) data. However the TDA tecnique has
not been applied to galaxy classification problem.
In this work the TDA method is applied to known galaxy classification catalogues
in order to evaluate whether this method can be used for large multi-wavelength datasets.