Deep Fusion of Remote Sensing Data for Accurate Classification

The multisensory fusion of remote sensing data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of m...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2017-08, Vol.14 (8), p.1253-1257
Main Authors: Yushi Chen, Chunyang Li, Ghamisi, Pedram, Xiuping Jia, Yanfeng Gu
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Classification
Convolutional neural network (CNN)
Data
data fusion
Data integration
Data mining
deep neural network (DNN)
Detection
Feature extraction
feature extraction (FE)
Frameworks
Fuses
hyperspectral image (HSI)
Laser radar
Lidar
light detection and ranging (LiDAR)
Multisensor fusion
multispectral image (MSI)
Neural networks
Neurons
Remote sensing
Satellites
Training
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Summary:The multisensory fusion of remote sensing data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyperspectral and light detection and ranging data. Then, a fully connected DNN is designed to fuse the heterogeneous features obtained by the previous CNNs. Through the aforementioned deep networks, one can extract the discriminant and invariant features of remote sensing data, which are useful for further processing. At last, logistic regression is used to produce the final classification results. Dropout and batch normalization strategies are adopted in the deep fusion framework to further improve classification accuracy. The obtained results reveal that the proposed deep fusion model provides competitive results in terms of classification accuracy. Furthermore, the proposed deep learning idea opens a new window for future remote sensing data fusion.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2017.2704625