A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment

Cong Zhang; Qi Wang; Xuelong Li

doi:10.1109/IGARSS.2019.8898659

A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment

Cong Zhang
, Qi Wang
, Xuelong Li

School of Artificial Intelligence, OPtics and Electronics

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

In this work, we propose a compact multi-task architecture based on deep learning for remote sensing scene classification and image quality assessment (IQA) simultaneously. The model can be trained in an end-to-end manner, and the robustness of classification is improved in our method. More importantly, by exploiting IQA and super-resolution, the accurate classification results can be obtained even if the images are distorted or with low quality. To the best of our knowledge, it is the first successful attempt to associate IQA with scene classification in a unified multi-task architecture. Our method is evaluated on the expanded UC Merced Land-Use dataset after data augmentation. In comparison with some other methods, the experimental results show that the proposed structure makes a great improvement on both classification and IQA.

Original language	English
Title of host publication	2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	10055-10058
Number of pages	4
ISBN (Electronic)	9781538691540
DOIs	https://doi.org/10.1109/IGARSS.2019.8898659
State	Published - Jul 2019
Event	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Yokohama, Japan Duration: 28 Jul 2019 → 2 Aug 2019

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference	39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Country/Territory	Japan
City	Yokohama
Period	28/07/19 → 2/08/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

Remote sensing
deep learning
image quality assessment
image super-resolution
multi-task learning
scene classification

Access to Document

10.1109/IGARSS.2019.8898659

Cite this

Zhang, C., Wang, Q., & Li, X. (2019). A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings (pp. 10055-10058). Article 8898659 (International Geoscience and Remote Sensing Symposium (IGARSS)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2019.8898659

Zhang, Cong ; Wang, Qi ; Li, Xuelong. / A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment. 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 10055-10058 (International Geoscience and Remote Sensing Symposium (IGARSS)).

@inproceedings{4a421638e98a475285e96bae9ce667b5,

title = "A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment",

abstract = "In this work, we propose a compact multi-task architecture based on deep learning for remote sensing scene classification and image quality assessment (IQA) simultaneously. The model can be trained in an end-to-end manner, and the robustness of classification is improved in our method. More importantly, by exploiting IQA and super-resolution, the accurate classification results can be obtained even if the images are distorted or with low quality. To the best of our knowledge, it is the first successful attempt to associate IQA with scene classification in a unified multi-task architecture. Our method is evaluated on the expanded UC Merced Land-Use dataset after data augmentation. In comparison with some other methods, the experimental results show that the proposed structure makes a great improvement on both classification and IQA.",

keywords = "Remote sensing, deep learning, image quality assessment, image super-resolution, multi-task learning, scene classification",

author = "Cong Zhang and Qi Wang and Xuelong Li",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",

year = "2019",

month = jul,

doi = "10.1109/IGARSS.2019.8898659",

language = "英语",

series = "International Geoscience and Remote Sensing Symposium (IGARSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "10055--10058",

booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",

}

Zhang, C, Wang, Q & Li, X 2019, A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment. in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings., 8898659, International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., pp. 10055-10058, 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, 28/07/19. https://doi.org/10.1109/IGARSS.2019.8898659

A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment. / Zhang, Cong; Wang, Qi; Li, Xuelong.
2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 10055-10058 8898659 (International Geoscience and Remote Sensing Symposium (IGARSS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment

AU - Zhang, Cong

AU - Wang, Qi

AU - Li, Xuelong

PY - 2019/7

Y1 - 2019/7

N2 - In this work, we propose a compact multi-task architecture based on deep learning for remote sensing scene classification and image quality assessment (IQA) simultaneously. The model can be trained in an end-to-end manner, and the robustness of classification is improved in our method. More importantly, by exploiting IQA and super-resolution, the accurate classification results can be obtained even if the images are distorted or with low quality. To the best of our knowledge, it is the first successful attempt to associate IQA with scene classification in a unified multi-task architecture. Our method is evaluated on the expanded UC Merced Land-Use dataset after data augmentation. In comparison with some other methods, the experimental results show that the proposed structure makes a great improvement on both classification and IQA.

AB - In this work, we propose a compact multi-task architecture based on deep learning for remote sensing scene classification and image quality assessment (IQA) simultaneously. The model can be trained in an end-to-end manner, and the robustness of classification is improved in our method. More importantly, by exploiting IQA and super-resolution, the accurate classification results can be obtained even if the images are distorted or with low quality. To the best of our knowledge, it is the first successful attempt to associate IQA with scene classification in a unified multi-task architecture. Our method is evaluated on the expanded UC Merced Land-Use dataset after data augmentation. In comparison with some other methods, the experimental results show that the proposed structure makes a great improvement on both classification and IQA.

KW - Remote sensing

KW - deep learning

KW - image quality assessment

KW - image super-resolution

KW - multi-task learning

KW - scene classification

UR - https://www.scopus.com/pages/publications/85077705053

U2 - 10.1109/IGARSS.2019.8898659

DO - 10.1109/IGARSS.2019.8898659

M3 - 会议稿件

AN - SCOPUS:85077705053

T3 - International Geoscience and Remote Sensing Symposium (IGARSS)

SP - 10055

EP - 10058

BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019

Y2 - 28 July 2019 through 2 August 2019

ER -

Zhang C, Wang Q, Li X. A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment. In 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 10055-10058. 8898659. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2019.8898659

A Multi-Task Architecture for Remote Sensing by Joint Scene Classification and Image Quality Assessment

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this