  Semantic segmentation is a crucial step in many Earth observation tasks.
Large quantity of pixel-level annotation is required to train deep networks for
semantic segmentation. Earth observation techniques are applied to varieties of
applications and since classes vary widely depending on the applications,
therefore, domain knowledge is often required to label Earth observation
images, impeding availability of labeled training data in many Earth
observation applications. To tackle these challenges, in this paper we propose
an unsupervised semantic segmentation method that can be trained using just a
single unlabeled scene. Remote sensing scenes are generally large. The proposed
method exploits this property to sample smaller patches from the larger scene
and uses deep clustering and contrastive learning to refine the weights of a
lightweight deep model composed of a series of the convolution layers along
with an embedded channel attention. After unsupervised training on the target
image/scene, the model automatically segregates the major classes present in
the scene and produces the segmentation map. Experimental results on the
Vaihingen dataset demonstrate the efficacy of the proposed method.
