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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Remote Sensing Image Scene Classification Using Rearranged Local Features
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Remote Sensing Image Scene Classification Using Rearranged Local Features

机译：使用重新排列的局部特征进行遥感影像场景分类

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摘要

Remote sensing image scene classification is a fundamental problem, which aims to label an image with a specific semantic category automatically. Recently, deep learning methods have achieved competitive performance for remote sensing image scene classification, especially the methods based on a convolutional neural network (CNN). However, most of the existing CNN methods only use feature vectors of the last fully connected layer. They give more importance to global information and ignore local information of images. It is common that some images belong to different categories, although they own similar global features. The reason is that the category of an image may be highly related to local features, other than the global feature. To address this problem, a method based on rearranged local features is proposed in this paper. First, outputs of the last convolutional layer and the last fully connected layer are employed to depict the local and global information, respectively. After that, the remote sensing images are clustered to several collections using their global features. For each collection, local features of an image are rearranged according to their similarities with local features of the cluster center. In addition, a fusion strategy is proposed to combine global and local features for enhancing the image representation. The proposed method surpasses the state of the arts on four public and challenging data sets: UC-Merced, WHU-RS19, Sydney, and AID.
机译：遥感图像场景分类是一个基本问题，其目的是自动为图像标注特定的语义类别。近年来，深度学习方法在遥感图像场景分类方面取得了竞争性性能，尤其是基于卷积神经网络（CNN）的方法。但是，大多数现有的CNN方法仅使用最后一个完全连接层的特征向量。它们更加重视全局信息，而忽略图像的局部信息。尽管有些图像具有相似的全局特征，但它们属于不同类别是很常见的。原因是图像的类别可能与全局特征以外的其他局部特征高度相关。为了解决这个问题，本文提出了一种基于局部特征重排的方法。首先，最后卷积层和最后完全连接层的输出分别用于描述局部信息和全局信息。之后，利用遥感图像的全局特征将其聚类为几个集合。对于每个集合，将根据图像的局部特征与聚类中心的局部特征的相似性重新排列图像。另外，提出了一种融合策略以结合全局和局部特征以增强图像表示。所提出的方法在UC-Merced，WHU-RS19，Sydney和AID四个公开且具有挑战性的数据集上超越了现有技术。

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