Multi-instance Reservoir Sampling and Selection for Online Continual Detection over VHR Remote Sensing Images

Jie Jiang, Zhen Han, Sheng Wang, C. Wang
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Abstract

Convolutional neural networks (CNNs) have shown outstanding performance in object detection over very-high-resolution (VHR) remote sensing images. However, the regular offline learning mode suffers from catastrophic forgetting problems and performs poorly on the non-stationary and never-ending data. To address this issue, a multi-instance reservoir sampling and selection method (MIRSS) is proposed for the continual detection on continuously generated remote sensing images. A multi-instance reservoir sampling module is used to build a size-fixed buffer and stores the previously learned samples for memory consolidation. Meanwhile, the situation that several objects may exist in each class of an image is focused. Moreover, samples in the buffer are selected with the reservoir selection module for retraining detectors. The experimental results based on three publicly available VHR satellite images, including images from the NWPU VHR-10, RSOD and DOTA data sets, highlight the effectiveness and practicality of the method.
VHR遥感图像在线连续检测的多实例库采样与选择
卷积神经网络(cnn)在高分辨率(VHR)遥感图像的目标检测中表现出优异的性能。然而,常规的离线学习模式存在灾难性的遗忘问题,并且在非平稳和永无止境的数据上表现不佳。针对这一问题,提出了一种多实例库采样与选择方法(MIRSS),用于对连续生成的遥感图像进行连续检测。使用多实例库采样模块建立固定大小的缓冲区,存储之前学习的样本用于记忆巩固。同时,重点分析了图像中每一类中可能存在多个目标的情况。此外,使用储层选择模块选择缓冲区中的样本用于再训练检测器。基于NWPU VHR-10、RSOD和DOTA数据集三张公开的VHR卫星图像的实验结果,验证了该方法的有效性和实用性。
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