Zhang Pei, Li Ying, Wang Dong, Wang Jiyue
School of Computer Science, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech & Image Information Processing, Northwestern Polytechnical University, Xi'an 710129, China.
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China.
Sensors (Basel). 2021 Feb 24;21(5):1566. doi: 10.3390/s21051566.
While growing instruments generate more and more airborne or satellite images, the bottleneck in remote sensing (RS) scene classification has shifted from data limits toward a lack of ground truth samples. There are still many challenges when we are facing unknown environments, especially those with insufficient training data. Few-shot classification offers a different picture under the umbrella of meta-learning: digging rich knowledge from a few data are possible. In this work, we propose a method named RS-SSKD for few-shot RS scene classification from a perspective of generating powerful representation for the downstream meta-learner. Firstly, we propose a novel two-branch network that takes three pairs of original-transformed images as inputs and incorporates Class Activation Maps (CAMs) to drive the network mining, the most relevant category-specific region. This strategy ensures that the network generates discriminative embeddings. Secondly, we set a round of self-knowledge distillation to prevent overfitting and boost the performance. Our experiments show that the proposed method surpasses current state-of-the-art approaches on two challenging RS scene datasets: NWPU-RESISC45 and RSD46-WHU. Finally, we conduct various ablation experiments to investigate the effect of each component of the proposed method and analyze the training time of state-of-the-art methods and ours.
随着成像仪器生成越来越多的航空或卫星图像，遥感（RS）场景分类的瓶颈已从数据限制转向缺乏地面真值样本。当我们面对未知环境，尤其是那些训练数据不足的环境时，仍然存在许多挑战。少样本分类在元学习的框架下提供了一种不同的思路：从少量数据中挖掘丰富的知识是可能的。在这项工作中，我们从为下游元学习器生成强大表示的角度出发，提出了一种名为RS-SSKD的方法用于少样本RS场景分类。首先，我们提出了一种新颖的双分支网络，该网络以三对原始-变换图像作为输入，并结合类激活映射（CAM）来驱动网络挖掘最相关的特定类别区域。这种策略确保网络生成有判别力的嵌入。其次，我们设置了一轮自知识蒸馏来防止过拟合并提高性能。我们的实验表明，该方法在两个具有挑战性的RS场景数据集NWPU-RESISC45和RSD46-WHU上超越了当前的最先进方法。最后，我们进行了各种消融实验来研究该方法各组件的效果，并分析了最先进方法和我们方法的训练时间。
