Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning

Dai Xueyuan; Wu Xiaofeng; Wang Bin; Zhang Liming

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Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning

机译：遥感影像的半监督场景分类：基于卷积神经网络和集成学习的方法

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摘要

The scarcity of labeled samples has been the main obstacle to the development of scene classification for remote sensing images. To alleviate this problem, the efforts have been dedicated to semisupervised classification which exploits both labeled and unlabeled samples for training classifiers. In this letter, we propose a novel semisupervised method that utilizes the effective residual convolutional neural network (ResNet) to extract preliminary image features. Moreover, the strategy of ensemble learning (EL) is adopted to establish discriminative image representations by exploring the intrinsic information of all available data. Finally, supervised learning is performed for scene classification. To verify the effectiveness of the proposed method, it is further compared with several state-of-the-art feature representation and semisupervised classification approaches. The experimental results show that by combining ResNet features with EL, the proposed method can obtain more effective image representations and achieve superior results.

机译：标记样品的稀缺性已成为发展遥感图像场景分类的主要障碍。为了减轻这个问题，已经致力于半监督分类，其利用标记的和未标记的样本来训练分类器。在这封信中，我们提出了一种新颖的半监督方法，该方法利用有效的残差卷积神经网络（ResNet）提取初步的图像特征。此外，采用集成学习（EL）策略，通过探索所有可用数据的内在信息来建立有区别的图像表示。最后，进行监督学习以进行场景分类。为了验证该方法的有效性，将其与几种最新的特征表示和半监督分类方法进行了比较。实验结果表明，通过将ResNet特征与EL结合，该方法可以获得更有效的图像表示，并取得了较好的效果。

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《IEEE Geoscience and Remote Sensing Letters》 |2019年第6期|869-873|共5页
作者
Dai Xueyuan; Wu Xiaofeng; Wang Bin; Zhang Liming;
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作者单位

Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China|Fudan Univ, Res Ctr Smart Networks & Syst, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China;

Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China|Fudan Univ, Res Ctr Smart Networks & Syst, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China;

Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China|Fudan Univ, Res Ctr Smart Networks & Syst, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China;

Fudan Univ, Key Lab Informat Sci Electromagnet Waves MoE, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China|Fudan Univ, Res Ctr Smart Networks & Syst, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China;

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关键词
Convolutional neural networks (CNNs); ensemble learning (EL); remote sensing (RS) images; scene classification; Semi-supervised classification;

机译：卷积神经网络（CNNS）;集合学习（EL）;遥感（RS）图像;场景分类;半监督分类;

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专利

1. Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning [J] . Dai Xueyuan, Wu Xiaofeng, Wang Bin, IEEE Geoscience and Remote Sensing Letters . 2019,第6期

机译：遥感图像的半动化场景分类：一种基于卷积神经网络和集合学习的方法
2. A multi-model ensemble method based on convolutional neural networks for aircraft detection in large remote sensing images [J] . Zhang Yuhang, Fu Kun, Sun Hao, Remote sensing letters . 2018,第1a3期

机译：基于卷积神经网络的多模型集成方法在大型遥感图像中的飞机检测
3. Convolutional neural network based heterogeneous transfer learning for remote-sensing scene classification [J] . Zhao Huizhen, Liu Fuxian, Zhang Han, International journal of remote sensing . 2019,第21a22期

机译：基于遥感场景分类的卷积神经网络的异构转移学习
4. A Decision-Level Fusion Method Based on Convolutional Neural Networks for Remote Sensing Scene Classification [C] . Bitao Jiang, Xiaobin Li, Tong Sun, IEEE Advanced Information Technology, Electronic and Automation Control Conference . 2018

机译：基于卷积神经网络的遥感场景分类决策级融合方法
5. Classification of SAR images and retrieval of remotely sensed parameters using neural networks. [D] . Rawat, Vikram Kumar. 1994

机译：SAR图像分类和使用神经网络检索遥感参数。
6. Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification [O] . Jianming Zhang, Chaoquan Lu, Jin Wang, 2020

机译：利用多尺寸图像和三重态损失训练卷积神经网络进行遥感场景分类
7. High Spatial Resolution Remote Sensing Scene Classification Method using Transfer Learning and Deep Convolutional Neural Network [O] . Wenmei Li, Juan Wang, Ziteng Wang, 2020

机译：高空间分辨率遥感场景分类方法，使用转移学习和深卷积神经网络

Semisupervised Scene Classification for Remote Sensing Images: A Method Based on Convolutional Neural Networks and Ensemble Learning

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