English(EN) Component Pooling Trained Across Orientations for Few-Shot Remote Sensing Scene Classification

新型RoCoP网络改进了少样本遥感场景分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了RoCoP，一种用于少样本遥感场景分类的新型网络，解决了现有方法的局限性。RoCoP采用一种跨各种方向训练的无参数组件池化方法，通过对目标特征进行分组并考虑航拍图像中缺乏典型方向的问题来提高准确性。在NWPU-RESISC45和AID等基准数据集上的实验证明了RoCoP的有效性，与强大的迁移学习基线相比，在5路1样本准确率方面有了显著提高。 AI

影响引入了一种少样本遥感学习的新方法，有望提高专业数据集的分类准确性。

排序理由详细介绍特定计算机视觉任务新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Bingchen Yan, Wenheng Guo, Yuye Zhou · 2026-10-27 04:00

跨方向训练的组件池用于少样本遥感场景分类

arXiv:2610.33518v1 Announce Type: new Abstract: Few-shot remote sensing scene classification (FSRSSC) aims to recognize novel scene categories from only a few labeled images. Most FSRSSC methods summarize the feature map of an image by global average pooling, which treats the few…

本文如何被排名

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跨方向训练的组件池用于少样本遥感场景分类

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