Deep Multiple Instance Learning-Based Spatial-Spectral Classification for PAN and MS Imagery

Panchromatic (PAN) and multispectral (MS) imagery classification is one of the hottest topics in the field of remote sensing. In recent years, deep learning techniques have been widely applied in many areas of image processing. In this paper, an end-to-end learning framework based on deep multiple i...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 56; no. 1; pp. 461 - 473
Main Authors: Liu, Xu, Jiao, Licheng, Zhao, Jiaqi, Zhao, Jin, Zhang, Dan, Liu, Fang, Yang, Shuyuan, Tang, Xu
Format: Journal Article
Language:English
Published: New York IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Classification
Convolution
Deep learning
Feature extraction
feature fusion
Frameworks
Fuses
Image classification
Image processing
Imagery
joint features
Machine learning
multiple instance learning
Multisensor fusion
Neural networks
Remote sensing
Spatial data
Spatial discrimination learning
Spatial resolution
Spectra
Spectral classification
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:Panchromatic (PAN) and multispectral (MS) imagery classification is one of the hottest topics in the field of remote sensing. In recent years, deep learning techniques have been widely applied in many areas of image processing. In this paper, an end-to-end learning framework based on deep multiple instance learning (DMIL) is proposed for MS and PAN images' classification using the joint spectral and spatial information based on feature fusion. There are two instances in the proposed framework: one instance is used to capture the spatial information of PAN and the other is used to describe the spectral information of MS. The features obtained by the two instances are concatenated directly, which can be treated as simple fusion features. To fully fuse the spatial-spectral information for further classification, the simple fusion features are fed into a fusion network with three fully connected layers to learn the high-level fusion features. Classification experiments carried out on four different airborne MS and PAN images indicate that the classifier provides feasible and efficient solution. It demonstrates that DMIL performs better than using a convolutional neural network and a stacked autoencoder network separately. In addition, this paper shows that the DMIL model can learn and fuse spectral and spatial information effectively, and has huge potential for MS and PAN imagery classification.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2017.2750220