Few-Shot Ship Classification in Optical Remote Sensing Images Using Nearest Neighbor Prototype Representation

With the development of ship detection in optical remote sensing images, it is convenient to obtain accurate detection results and ship images. Owing to the superior performance of convolutional neural networks (CNNs), one way to acquire the category of ship is to train a classifier using numerous ship images. However, the classification performance of CNN may degrade in the case of a small number of training samples. To solve this problem, we propose a metric-based few-shot method to generate novel concept (class) representation using nearest neighbor prototype. Different from image-to-image measure in common few-shot methods, we use an image-to-feature measure. We map small number of samples to the feature space through CNN, and generate prototypes by computing nearest neighbor value on each dimension of the feature separately. Our method is validated on patch-level ship image dataset, a reproduced ship classification dataset based on HRSC2016. The experimental results demonstrate the accuracy and robustness of our method for ship classification with a small amount of labeled data.