Abstract Remote sensing image retrieval is aimed at obtaining a target image from a typically large number of remote sensing images. Different from natural scene images, remote sensing images usually includes more channels such as near-infrared reflectances (NIR), which will result in higher data dimensionality. However, high dimensionality of image features may lead to information redundancy and high computational costs. Recently, deep hashing methods to reduce image feature dimensionality have been proposed. However, compared with natural images, remote sensing images have smaller interclass distances, which hinder the application of deep hashing methods for image retrieval. To overcome this problem, in this paper, we develop a slice-feature deep hashing (SFDH) method for remote sensing image retrieval. The major contribution of this work is proposing an image correlation reduction strategy by separating the features in the fully connected layers into many slices. The SFDH architecture is based on a pre-trained Inception V3 model; the proposed model not only extracts image features from remote sensing images, but also introduces the slice-feature strategy to improve the fully connected structure of the previously proposed metric learning based deep hashing network (MiLaN). Besides, triplet loss is utilized to further enlarge differences between classes. Our experimental results demonstrate that the proposed slice-feature strategy outperforms state-of-the-art remote sensing image retrieval methods.

中文翻译：

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基于切片特征的遥感图像检索深度散列算法

摘要 遥感图像检索旨在从典型的大量遥感图像中获取目标图像。与自然场景图像不同，遥感图像通常包含更多的通道，如近红外反射（NIR），这将导致更高的数据维度。然而，图像特征的高维可能导致信息冗余和高计算成本。最近，已经提出了降低图像特征维数的深度哈希方法。然而，与自然图像相比，遥感图像具有更小的类间距离，这阻碍了深度哈希方法在图像检索中的应用。为了克服这个问题，在本文中，我们开发了一种用于遥感图像检索的切片特征深度哈希（SFDH）方法。这项工作的主要贡献是通过将全连接层中的特征分成许多切片，提出了一种图像相关性降低策略。SFDH 架构基于预训练的 Inception V3 模型；所提出的模型不仅从遥感图像中提取图像特征，而且还引入了切片特征策略来改进先前提出的基于度量学习的深度哈希网络（MiLaN）的全连接结构。此外，三重损失被用来进一步扩大类之间的差异。我们的实验结果表明，所提出的切片特征策略优于最先进的遥感图像检索方法。SFDH 架构基于预训练的 Inception V3 模型；所提出的模型不仅从遥感图像中提取图像特征，而且还引入了切片特征策略来改进先前提出的基于度量学习的深度哈希网络（MiLaN）的全连接结构。此外，三重损失被用来进一步扩大类之间的差异。我们的实验结果表明，所提出的切片特征策略优于最先进的遥感图像检索方法。SFDH 架构基于预训练的 Inception V3 模型；所提出的模型不仅从遥感图像中提取图像特征，而且还引入了切片特征策略来改进先前提出的基于度量学习的深度哈希网络（MiLaN）的全连接结构。此外，三重损失被用来进一步扩大类之间的差异。我们的实验结果表明，所提出的切片特征策略优于最先进的遥感图像检索方法。三重损失被用来进一步扩大类之间的差异。我们的实验结果表明，所提出的切片特征策略优于最先进的遥感图像检索方法。三重损失被用来进一步扩大类之间的差异。我们的实验结果表明，所提出的切片特征策略优于最先进的遥感图像检索方法。