By comparing and contrasting photographs of the same location taken at various periods using remote sensing technology, we may determine whether or not there have been any changes to the land cover there. Identifying shifts in a timely manner will facilitate the identification of natural disasters and climate change, the evaluation of damage, and the rehabilitation of affected areas. Both supervised and unsupervised methods have been utilised historically for change detection. In practise, specialists can only gather a small number of tagged patterns to use in solving the change detection issue. In this case, supervised techniques are ineffective, and if unsupervised methods are employed instead, some scarce but useful labels are wasted. In this dissertation, we offer a novel methodology to enhance change detection in the face of data scarcity by combining a small number of labelled patterns with a large number of unlabeled ones. In this study, many neural network architectures are employed to develop active and semi-supervised learning change detection methods. For better change detection when only a small number of labelled patterns are available, we redesign two unsupervised learning based neural networks, a modified self-organizing feature map and a Hopfield type neural network, in a semi-supervised framework.