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In remote sensing scene classification, manual annotation is time-consuming and expensive to collect, which limits the effective deployment of supervised classification networks. Semi-supervised learning (SSL), which can benefit from unlabelled and labelled samples simultaneously, attracts much attention. In this letter, we introduce a cluster group construction method for semi-supervised high-resolution remote sensing (HRRS) scene classification. The low-density separation assumption, which assumes that the decision boundary of a classification network should lie in the low-density region, is a vital assumption to guide the construction of clusters. Following this, we propose to construct inner-class dense neighbours (IDN) for the semi-supervised classification framework. The proposed IDN can increase the density of clusters for a better determination of high-density regions to help the network training in the SSL framework. The experiments on the NWPU-RESIS45 and RSSCN7 datasets demonstrate the effectiveness of our proposed method, which show that the performance of the classification network with our proposed IDN has a prominent classification accuracy improvement.

IDN: inner-class dense neighbours for semi-supervised learning-based remote sensing scene classification