MKN: Metakernel Networks for Few Shot Remote Sensing Scene Classification

Few-shot remote sensing scene classification tries to make a model quickly adapt to new scenes with only a few samples that do not appear in the closed training set. Since limited samples can hardly describe the distribution of data, it is a challenge for a model to learn good generalized features. Since limited samples are rarely representative, it is another challenge for a model to learn classification boundaries that depend on sample bias. Therefore, we propose a method called metakernel networks (MKNs) to solve the challenges via integrating a parametric linear classifier (PLC) into the metalearning framework to address the former problem assembling a metakernel strategy (MKS) and a stretching loss (Stloss) to address the later problem. The PLC learns prior knowledge from different tasks sampled from the same task family to learn rich features. The MKS is designed to remap low-dimensional indistinguishable features to a high-dimensional space to solve the low-dimensional feature entanglement caused by large intraclass differences between remote sensing images. The Stloss reduces the dependence of the hyperplane formed by a few points in each class on sample selection by reducing the intraclass and interclass variance ratios, thus solving the boundary fragility problem caused by interclass similarity. Experiments on three public datasets, UC_Merced, NWPU-RESISC45, and AID, show the state-of-the-art performance by the accuracy improvement of 5.04%, 4.81%, and 4.69% respectively. Our findings provide a new perspective by suggesting that the previously neglected issue of classification boundaries may be a key factor for few-shot remote sensing scene classification.