Personalized Multiparty Few-Shot Learning for Remote Sensing Scene Classification

The existing few-shot scene classification (FSSC) algorithms have achieved satisfactory results, but they are limited by the paradigm of centralized machine learning, i.e., private remote sensing data need to be centralized on a certain server for training. However, remote sensing images generally contain sensitive information such as national security and company privacy, so it is realistically difficult to collect remote sensing data from all the parties. Therefore, there is a pressing requirement in FSSC for a novel paradigm to achieve multiparty collaborative learning without compromising remote sensing data privacy. In this article, we formulate a novel personalized multiparty few-shot learning (PMPFSL) paradigm for remote sensing scene classification. In PMPFSL, different participants can achieve multiparty collaborative learning without sacrificing the privacy of their local data, and their respective local models are able to recognize the unseen remote sensing scene categories with a small number of labeled samples. Importantly, the proposed PMPFSL is applicable to various multiparty learning (MPL) algorithms and FSSC networks. Moreover, to address the problems of local model overfitting and poor discriminability of few-shot metrics, we propose the personalized adaptive distillation (PAD) scheme and multiscale feature matching network (MSFMNet) on PMPFSL, respectively. Specifically, each participant obtains the MSFMNet with initialization parameters, and implements a certain number of local training on their respective private machines. Global aggregation is subsequently achieved by uploading only the local models to the central server. In a new round of local training, the participants realize personalized data adaptation to the global model based on the PAD. Overall, the proposed PMPFSL customizes a personalized few-shot model for each participant that is more tailored to their respective remote sensing scenarios. The experimental results demonstrate that our PMPFSL is superior in three benchmark FSSC datasets. We also extensively studied and analyzed the contributions of PAD and MSFMNet in the proposed PMPFSL framework.