Two-Path Aggregation Attention Network With Quad-Patch Data Augmentation for Few-Shot Scene Classification
Maoguo Gong, Jianzhao Li, Yourun Zhang, Yue Wu, Mingyang Zhang
Abstract
The few-shot scene classification is dedicated to identifying unseen remote sensing classes when only a very small number of labeled samples are available for reference. Most of the existing few-shot scene classification methods are based on meta-learning and employ the episodic learning for training, which lacks the consideration for the utilization of data efficiency. In this paper, instead of designing sophisticated meta-learning based algorithms, we are committed to training a feature extractor with good generalization performance and strong feature extraction capability. Specifically, we propose a novel two-path aggregation attention network with quad-patch data augmentation, called DANet, to solve the problem of few-shot scene classification from both data and architecture aspects. In terms of data, we design a new data augmentation strategy named quad-patch augmentation. We utilize the characteristics of remote sensing images to chunk and reassemble any existing data, thereby generating pseudo-new data to enrich the training set. In terms of architecture, we present a two-path aggregation attention module that makes it easier for the model to focus on the key clues in a targeted manner. The comparative experiments in natural image datasets and remote sensing image datasets demonstrate the effectiveness of our two innovations. In addition, DANet achieves competitive or state-of-the-art (SOTA) results on three benchmark scene classification datasets.