In this article, an unsupervised domain adaptation strategy has been investigated using a deep Siamese neural network in scene-level land cover classification using remotely sensed images. At the onset, the soft class label and probability scores of each target sample have been obtained using a pretrained model of a deep convolutional neural network. Thereafter, a semiautomatic threshold selection algorithm along with a graph-based approach has been explored to obtain the "most-confident" target samples. Furthermore, the deep Siamese network has been incorporated by training the source and "most-confident" target samples to generate the classwise cross domain common subspace. To assess the effectiveness of the proposed framework, experiments are carried out using three aerial image datasets. The results are found to be encouraging for the proposed scheme in comparison with the other state-of-art techniques.