Learning Class-Aware Local Representations for Few-Shot Remote Sensing Scene Classification

Few-Shot Remote Sensing Scene Classification (FSRSSC) aims at recognizing novel categories with only a few labeled samples. Local representations can retain richer details than image-level feature vectors and adapt well to the characteristics of spatially scattered objects in remote sensing images. However, the existing local representation-based few-shot learning works generally overlook the role of class-semantic information. To address this issue, in this article, a Class-aware Local Representation Learning (CLRL) method is proposed for FSRSSC. It generates class-aware support local representations by calibrating the local features with class-level global features to alleviate the problem of ignoring the class-semantic information. The Image-to-Class (I2C) metric is used to measure the similarities between query samples and support samples. Moreover, a novel Local Representation-based Self-supervised Learning (LRSL) strategy is proposed. The main idea of LRSL is to maximize the relationship consistency between the query local representations and different views of support local representations. A novel consistency loss is further proposed as the objective function of LRSL to supervise the model training. Different from the existing image-level feature vector-based self-supervised learning tasks, the proposed LRSL forces the model to capture the structure information of local representations. A robust model can be trained by jointly optimizing LRSL tasks and FSRSSC tasks. The experimental results on the NWPU-RESISC45, WHU-RS19, and UC Merced datasets show the superiority of our proposed method.