Using CNN-based high-level features for remote sensing scene classification

Zhengzheng Fang; Wei Li; Jinyi Zou; Qian Du

doi:10.1109/IGARSS.2016.7729674

Using CNN-based high-level features for remote sensing scene classification

Zhengzheng Fang, Wei Li, Jinyi Zou, Qian Du

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

18 Citations (Scopus)

Abstract

In this paper, convolutional neural networks (CNNs) is employed for remote-sensing scene classification, which fully utilizes the semantic features extracted from the images while ignoring some traditional features. Consider the limited labeled samples, CaffeNet model as the pre-trained architecture is adopted. By fine-tuning the pre-trained models, the proposed method is expected to be robust and efficient. Its performance is evaluated with two remote-sensing scene datasets. From the experimental results, the proposed CNN-based scene classification method does provide more excellent performance and be superior to several state-of-the-art methods.

Original language	English
Title of host publication	2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2610-2613
Number of pages	4
ISBN (Electronic)	9781509033324
DOIs	https://doi.org/10.1109/IGARSS.2016.7729674
Publication status	Published - 1 Nov 2016
Externally published	Yes
Event	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Beijing, China Duration: 10 Jul 2016 → 15 Jul 2016

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2016-November

Conference

Conference	36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Country/Territory	China
City	Beijing
Period	10/07/16 → 15/07/16

Keywords

Scene classification
convolutional neural networks
deep learning
feature extraction

Access to Document

10.1109/IGARSS.2016.7729674

Cite this

Fang, Z., Li, W., Zou, J., & Du, Q. (2016). Using CNN-based high-level features for remote sensing scene classification. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings (pp. 2610-2613). Article 7729674 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2016.7729674

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title = "Using CNN-based high-level features for remote sensing scene classification",

abstract = "In this paper, convolutional neural networks (CNNs) is employed for remote-sensing scene classification, which fully utilizes the semantic features extracted from the images while ignoring some traditional features. Consider the limited labeled samples, CaffeNet model as the pre-trained architecture is adopted. By fine-tuning the pre-trained models, the proposed method is expected to be robust and efficient. Its performance is evaluated with two remote-sensing scene datasets. From the experimental results, the proposed CNN-based scene classification method does provide more excellent performance and be superior to several state-of-the-art methods.",

keywords = "Scene classification, convolutional neural networks, deep learning, feature extraction",

author = "Zhengzheng Fang and Wei Li and Jinyi Zou and Qian Du",

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doi = "10.1109/IGARSS.2016.7729674",

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

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Fang, Z, Li, W, Zou, J & Du, Q 2016, Using CNN-based high-level features for remote sensing scene classification. in 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings., 7729674, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2016-November, Institute of Electrical and Electronics Engineers Inc., pp. 2610-2613, 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016, Beijing, China, 10/07/16. https://doi.org/10.1109/IGARSS.2016.7729674

Using CNN-based high-level features for remote sensing scene classification. / Fang, Zhengzheng; Li, Wei; Zou, Jinyi et al.
2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2610-2613 7729674 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2016-November).

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

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N2 - In this paper, convolutional neural networks (CNNs) is employed for remote-sensing scene classification, which fully utilizes the semantic features extracted from the images while ignoring some traditional features. Consider the limited labeled samples, CaffeNet model as the pre-trained architecture is adopted. By fine-tuning the pre-trained models, the proposed method is expected to be robust and efficient. Its performance is evaluated with two remote-sensing scene datasets. From the experimental results, the proposed CNN-based scene classification method does provide more excellent performance and be superior to several state-of-the-art methods.

AB - In this paper, convolutional neural networks (CNNs) is employed for remote-sensing scene classification, which fully utilizes the semantic features extracted from the images while ignoring some traditional features. Consider the limited labeled samples, CaffeNet model as the pre-trained architecture is adopted. By fine-tuning the pre-trained models, the proposed method is expected to be robust and efficient. Its performance is evaluated with two remote-sensing scene datasets. From the experimental results, the proposed CNN-based scene classification method does provide more excellent performance and be superior to several state-of-the-art methods.

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KW - convolutional neural networks

KW - deep learning

KW - feature extraction

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M3 - Conference contribution

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BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings

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Fang Z, Li W, Zou J, Du Q. Using CNN-based high-level features for remote sensing scene classification. In 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2610-2613. 7729674. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2016.7729674

Using CNN-based high-level features for remote sensing scene classification

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