基于密集连接卷积神经网络和集成分类器的遥感图像场景分类

IF 1 4区 地球科学 Q4 GEOGRAPHY, PHYSICAL
Q. Cheng, Yuan Xu, Peng Fu, Jinling Li, Wen Wang, Y. Ren
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引用次数: 2

摘要

深度学习技术,特别是卷积神经网络,在很大程度上提高了分析和理解遥感图像的性能。然而,现有的场景分类方法通常忽略了对遥感图像的场景分类至关重要的局部和空间信息。在本研究中,提出了一种基于预训练的密集连接卷积神经网络和集成分类器的遥感图像场景分类方法,以解决图像分类中局部和空间信息利用不足的问题。具体来说,我们首先利用预训练的DenseNet并对其进行微调,以释放其在遥感图像特征表示中的潜力。其次,利用空间金字塔结构和改进的Fisher矢量编码策略来进一步增强从卷积层捕获的特征图的表示能力和鲁棒性。然后,考虑到对特征描述符的关注度较低,我们在网络架构中集成了集成分类器。进行了大量的实验,该方法在UC Merced、AID和NWPU-RESSC45数据集上取得了优异的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scene Classification of Remotely Sensed Images via Densely Connected Convolutional Neural Networks and an Ensemble Classifier
Deep learning techniques, especially convolutional neural networks, have boosted performance in analyzing and understanding remotely sensed images to a great extent. However, existing scene-classification methods generally neglect local and spatial information that is vital to scene classification of remotely sensed images. In this study, a method of scene classification for remotely sensed images based on pretrained densely connected convolutional neural networks combined with an ensemble classifier is proposed to tackle the under-utilization of local and spatial information for image classification. Specifically, we first exploit the pretrained DenseNet and fine-tuned it to release its potential in remote-sensing image feature representation. Second, a spatial-pyramid structure and an improved Fisher-vector coding strategy are leveraged to further strengthen representation capability and the robustness of the feature map captured from convolutional layers. Then we integrate an ensemble classifier in our network architecture considering that lower attention to feature descriptors. Extensive experiments are conducted, and the proposed method achieves superior performance on UC Merced, AID, and NWPU-RESISC45 data sets.
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来源期刊
Photogrammetric Engineering and Remote Sensing
Photogrammetric Engineering and Remote Sensing 地学-成像科学与照相技术
CiteScore
1.70
自引率
15.40%
发文量
89
审稿时长
9 months
期刊介绍: Photogrammetric Engineering & Remote Sensing commonly referred to as PE&RS, is the official journal of imaging and geospatial information science and technology. Included in the journal on a regular basis are highlight articles such as the popular columns “Grids & Datums” and “Mapping Matters” and peer reviewed technical papers. We publish thousands of documents, reports, codes, and informational articles in and about the industries relating to Geospatial Sciences, Remote Sensing, Photogrammetry and other imaging sciences.
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