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Title: Domain Adaptation for Convolutional Neural Networks Based Remote Sensing Scene Classification

Abstract

Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the remote sensing techniques, a large number of RS scene images are available. Since manually labeling largescale RS scene images is both labor and time consuming, when a new unlabeled dataset is obtained, how to use the existing labeled datasets to classify the new unlabeled images is an important research direction. Different RS scene image datasets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions and image scales, so the distribution discrepancy exists among different image datasets. As a result, simply applying Convolutional Neural Networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain Adaptation (DA) can be helpful to solve this problem. In this paper, we design a Subspace Alignment (SA) and CNN based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Finetuning the modified CNN model with the added SA layer is able to make themore » CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public datasets show that adding the SA layer into CNN can improve the scene classification on target domain.« less

Authors:
 [1];  [2];  [1];  [1];  [3];  [4]
  1. Beijing Jiaotong Univ., Beijing (China)
  2. Univ. of Texas, Edinburg, TX (United States)
  3. Brookhaven National Lab. (BNL), Upton, NY (United States)
  4. Univ. of South Carolina, Columbia, SC (United States); Tianjin Univ. (China)
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)
OSTI Identifier:
1492766
Report Number(s):
BNL-210923-2019-JAAM
Journal ID: ISSN 1545-598X
Grant/Contract Number:  
SC0012704
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Geoscience and Remote Sensing Letters
Additional Journal Information:
Journal Name: IEEE Geoscience and Remote Sensing Letters; Journal ID: ISSN 1545-598X
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Song, Shaoyue, Yu, Hongkai, Miao, Zhenjiang, Zhang, Qiang, Lin, Yuewei, and Wang, Song. Domain Adaptation for Convolutional Neural Networks Based Remote Sensing Scene Classification. United States: N. p., 2019. Web. doi:10.1109/LGRS.2019.2896411.
Song, Shaoyue, Yu, Hongkai, Miao, Zhenjiang, Zhang, Qiang, Lin, Yuewei, & Wang, Song. Domain Adaptation for Convolutional Neural Networks Based Remote Sensing Scene Classification. United States. doi:10.1109/LGRS.2019.2896411.
Song, Shaoyue, Yu, Hongkai, Miao, Zhenjiang, Zhang, Qiang, Lin, Yuewei, and Wang, Song. Mon . "Domain Adaptation for Convolutional Neural Networks Based Remote Sensing Scene Classification". United States. doi:10.1109/LGRS.2019.2896411.
@article{osti_1492766,
title = {Domain Adaptation for Convolutional Neural Networks Based Remote Sensing Scene Classification},
author = {Song, Shaoyue and Yu, Hongkai and Miao, Zhenjiang and Zhang, Qiang and Lin, Yuewei and Wang, Song},
abstractNote = {Remote sensing (RS) scene classification plays an important role in the field of earth observation. With the rapid development of the remote sensing techniques, a large number of RS scene images are available. Since manually labeling largescale RS scene images is both labor and time consuming, when a new unlabeled dataset is obtained, how to use the existing labeled datasets to classify the new unlabeled images is an important research direction. Different RS scene image datasets may be taken from different type of sensors, and the images may vary from imaging modalities, spatial resolutions and image scales, so the distribution discrepancy exists among different image datasets. As a result, simply applying Convolutional Neural Networks (CNN) trained on source domain cannot accurately classify the images on target domain. Domain Adaptation (DA) can be helpful to solve this problem. In this paper, we design a Subspace Alignment (SA) and CNN based framework to solve the DA problem in RS scene image classification. A new SA layer is proposed and added into CNN models for DA, which could align the source and target domains in some feature subspace. Finetuning the modified CNN model with the added SA layer is able to make the CNN model adapt to the aligned feature subspace and helps to relieve the domain distribution discrepancy. The experiments conducted on two public datasets show that adding the SA layer into CNN can improve the scene classification on target domain.},
doi = {10.1109/LGRS.2019.2896411},
journal = {IEEE Geoscience and Remote Sensing Letters},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {2}
}

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This content will become publicly available on February 25, 2020
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