Ir-Ssl: Improved Regularization Based Semi-Supervised Learning For Land Cover Classification

Land cover classification has significant contributions in several applications including natural calamities estimation and response, observation of environmental changes, and urban planning to name a few. These types of applications demand the identification of different categories of land cover. Traditional land cover classification is significantly dependent on the availability of huge amount of labeled data. However, labeling satellite data is very time consuming and it often requires expert knowledge. To alleviate the dependency on labeled data, we propose a novel and improved regularization based deep semi-supervised learning (IR-SSL) method for land cover classification. Adaptation of deep semi-supervised learning approach in such a task gains reliability due to its robustness in feature learning. To consolidate the performance of our deep semi-supervised learning method, we combine it with a robust data augmentation technique. We perform experiments on a benchmark dataset. Considering limited labeled samples from the dataset, our method outperforms many state-of-the-art models.