Deep Updated Subspace Networks for Few-Shot Remote Sensing Scene Classification
Anyong Qin, Fuyang Chen, Qiang Li, Lingyun Tang, Feng Yang, Yue Zhao, Chenqiang Gao
Abstract
Due to the difficulty of manually labeling remote sensing scene images and the demand for the ability to recognize new scene classes, few-shot remote sensing scene classification (FSRSSC) has attracted more and more attention. At present, metric-based FSRSSC methods have made promising progress, especially the prototypical networks-based methods. However, due to the complexity of the background of remote sensing scene images, the prototype classifier, which takes the average features of support samples as the metric benchmark, retains the features of category-irrelevant objects and other background information in the image. This leads to a bad classification result. Therefore, in this work, we propose a FSRSSC method based on the deep updated subspace network (DUSN), which uses class subspace as a metric benchmark to represent the commonality of a category and can effectively mitigate the negative impact of irrelevant objects on the classifier. In addition, for the higher inter-class similarity and larger intra-class variance of remote sensing scene images, we further propose an inter-class constraint and an intra-class constraint to mitigate the classification confusion. We leverage the inter-class constraint to make the images of different classes as far apart as possible, and the intra-class constraint to keep the images of the same class clustered as closely together as possible. Experimental results on three public benchmark datasets demonstrate that our method performs better than the state-of-the-art methods for FSRSSC.