Ensemble model with cascade attention mechanism for high-resolution remote sensing image scene classification

Fengpeng Li; Fengpeng Li; Ruyi Feng; Ruyi Feng; Wei Han; Wei Han; Lizhe Wang; Lizhe Wang

doi:10.1364/OE.395866

Optics Express
Vol. 28,
Issue 15,
pp. 22358-22387
(2020)
•https://doi.org/10.1364/OE.395866

Ensemble model with cascade attention mechanism for high-resolution remote sensing image scene classification

Fengpeng Li, Ruyi Feng, Wei Han, and Lizhe Wang

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Fengpeng Li, Ruyi Feng, Wei Han, and Lizhe Wang, "Ensemble model with cascade attention mechanism for high-resolution remote sensing image scene classification," Opt. Express 28, 22358-22387 (2020)

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Abstract

Scene classification of high-resolution remote sensing images is a fundamental task of earth observation. And numerous methods have been proposed to achieve this. However, these models are inadequate as the number of labelled training data limits them. Most of the existing methods entirely rely on global information, while regions with class-specific ground objects determine the categories of high-resolution remote sensing images. An ensemble model with a cascade attention mechanism, which consists of two kinds of the convolutional neural network, is proposed to address these issues. To improve the generality of the feature extractor, each branch is trained on different large datasets to enrich the prior knowledge. Moreover, to force the model to focus on the most class-specific region in each high-resolution remote sensing image, a cascade attention mechanism is proposed to combine the branches and capture the most discriminative information. By experiments on four benchmark datasets, OPTIMAL-31, UC Merced Land-Use Dataset, Aerial Image Dataset and NWPU-RESISC45, the proposed end-to-end model cascade attention-based double branches model in this paper achieves state-of-the-art performance on each benchmark dataset.

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2020 (3)

X.-Y. Tong, G.-S. Xia, Q. Lu, H. Shen, S. Li, S. You, and L. Zhang, “Land-cover classification with high-resolution remote sensing images using transferable deep models,” Remote Sens. Environ. 237, 111322 (2020).
[Crossref]

Y. Zhu, R. Li, Y. Yang, and N. Ye, “Learning cascade attention for fine-grained image classification,” Neural Netw. 122, 174–182 (2020).
[Crossref]

H. Sun, S. Li, X. Zheng, and X. Lu, “Remote sensing scene classification by gated bidirectional network,” IEEE Trans. Geosci. Remote. Sens. 58(1), 82–96 (2020).
[Crossref]

2019 (18)

L. Fan, T. Zhang, X. Zhao, H. Wang, and M. Zheng, “Deep topology network: A framework based on feedback adjustment learning rate for image classification,” Adv. Eng. Inform. 42, 100935 (2019).
[Crossref]

S. Saha, F. Bovolo, and L. Bruzzone, “Unsupervised deep change vector analysis for multiple-change detection in vhr images,” IEEE Trans. Geosci. Remote. Sens. 57(6), 3677–3693 (2019).
[Crossref]

B. Fang, Y. Li, H. Zhang, and J. C.-W. Chan, “Hyperspectral images classification based on dense convolutional networks with spectral-wise attention mechanism,” Remote Sens. 11(2), 159–163 (2019).
[Crossref]

W. Ma, Q. Yang, Y. Wu, W. Zhao, and X. Zhang, “Double-branch multi-attention mechanism network for hyperspectral image classification,” Remote Sens. 11(11), 1307–1328 (2019).
[Crossref]

X. Mei, E. Pan, Y. Ma, X. Dai, J. Huang, F. Fan, Q. Du, H. Zheng, and J. Ma, “Spectral-spatial attention networks for hyperspectral image classification,” Remote Sens. 11(8), 963–981 (2019).
[Crossref]

B. Zhang, Y. Zhang, and S. Wang, “A lightweight and discriminative model for remote sensing scene classification with multidilation pooling module,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 12(8), 2636–2653 (2019).
[Crossref]

G. Liu, Y. Gousseau, and F. Tupin, “A contrario comparison of local descriptors for change detection in very high spatial resolution satellite images of urban areas,” IEEE Trans. Geosci. Remote. Sens. 57(6), 3904–3918 (2019).
[Crossref]

J. Song, X. Tong, L. Wang, C. Zhao, and A. V. Prishchepov, “Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach,” Landsc. Urban Plan. 190, 103580 (2019).
[Crossref]

J. G. Su, P. Dadvand, M. J. Nieuwenhuijsen, X. Bartoll, and M. Jerrett, “Associations of green space metrics with health and behavior outcomes at different buffer sizes and remote sensing sensor resolutions,” Environ. Int. 126, 162–170 (2019).
[Crossref]

S. Wang, M. Garcia, P. Bauer-Gottwein, J. Jakobsen, P. J. Zarco-Tejada, F. Bandini, V. S. Paz, and A. Ibrom, “High spatial resolution monitoring land surface energy, water and co2 fluxes from an unmanned aerial system,” Remote Sens. of Environ. 229, 14–31 (2019).
[Crossref]

Z. Cao, R. Ma, H. Duan, and K. Xue, “Effects of broad bandwidth on the remote sensing of inland waters: Implications for high spatial resolution satellite data applications,” ISPRS-J. Photogramm. Remote Sens. 153, 110–122 (2019).
[Crossref]

T. Tuncer and S. Dogan, “Novel dynamic center based binary and ternary pattern network using m4 pooling for real world voice recognition,” Appl. Acoust. 156, 176–185 (2019).
[Crossref]

Y. Liu, C. Y. Suen, Y. Liu, and L. Ding, “Scene classification using hierarchical wasserstein cnn,” IEEE Trans. Geosci. Remote. Sens. 57(5), 2494–2509 (2019).
[Crossref]

Y. Yuan, J. Fang, X. Lu, and Y. Feng, “Remote sensing image scene classification using rearranged local features,” IEEE Trans. Geosci. Remote. Sens. 57(3), 1779–1792 (2019).
[Crossref]

R. Minetto, M. P. Segundo, and S. Sarkar, “Hydra: an ensemble of convolutional neural networks for geospatial land classification,” IEEE Trans. Geosci. Remote. Sens. 57(9), 6530–6541 (2019).
[Crossref]

J. Xie, N. He, L. Fang, and A. Plaza, “Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification,” IEEE Trans. Geosci. Remote. Sens. 57(9), 6916–6928 (2019).
[Crossref]

Q. Wang, S. Liu, J. Chanussot, and X. Li, “Scene classification with recurrent attention of VHR remote sensing images,” IEEE Trans. Geosci. Remote. Sens. 57(2), 1155–1167 (2019).
[Crossref]

Y. Zhang, Y. Yuan, Y. Feng, and X. Lu, “Hierarchical and robust convolutional neural network for very high-resolution remote sensing object detection,” IEEE Trans. Geosci. Remote. Sens. 57(8), 5535–5548 (2019).
[Crossref]

2018 (12)

X. Lu, W. Zhang, and X. Li, “A hybrid sparsity and distance-based discrimination detector for hyperspectral images,” IEEE Trans. Geosci. Remote. Sens. 56(3), 1704–1717 (2018).
[Crossref]

W. Han, R. Feng, L. Wang, and Y. Cheng, “A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification,” ISPRS-J. Photogramm. Remote Sens. 145, 23–43 (2018).
[Crossref]

G. Cheng, C. Yang, X. Yao, L. Guo, and J. Han, “When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs,” IEEE Trans. Geosci. Remote. Sens. 56(5), 2811–2821 (2018).
[Crossref]

X. Lu, B. Wang, X. Zheng, and X. Li, “Exploring models and data for remote sensing image caption generation,” IEEE Trans. Geosci. Remote. Sens. 56(4), 2183–2195 (2018).
[Crossref]

G. Milani, M. Volpi, D. Tonolla, M. Doering, C. Robinson, M. Kneubühler, and M. Schaepman, “Robust quantification of riverine land cover dynamics by high-resolution remote sensing,” Remote. Sens. Environ. 217, 491–505 (2018).
[Crossref]

Y. Liu and C. Huang, “Scene classification via triplet networks,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 11(1), 220–237 (2018).
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B. Huang, B. Zhao, and Y. Song, “Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery,” Remote. Sens. Environ. 214, 73–86 (2018).
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2017 (10)

X. Bian, C. Chen, L. Tian, and Q. Du, “Fusing local and global features for high-resolution scene classification,” IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. 10(6), 2889–2901 (2017).
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2016 (4)

G. Cheng, P. Zhou, and J. Han, “Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images,” IEEE Trans. Geosci. Remote. Sens. 54(12), 7405–7415 (2016).
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Q. Zhu, Y. Zhong, B. Zhao, G.-S. Xia, and L. Zhang, “Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery,” IEEE Geosci. Remote Sens. Lett. 13(6), 747–751 (2016).
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2015 (4)

G. Cheng, P. Zhou, J. Han, L. Guo, and J. Han, “Auto-encoder-based shared mid-level visual dictionary learning for scene classification using very high resolution remote sensing images,” Int. J. Comput. Vis. 9(5), 639–647 (2015).
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2014 (2)

G. Cheng, J. Han, P. Zhou, and L. Guo, “Multi-class geospatial object detection and geographic image classification based on collection of part detectors,” ISPRS-J. Photogramm. Remote Sens. 98, 119–132 (2014).
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2013 (1)

V. Risojević and Z. Babić, “Fusion of global and local descriptors for remote sensing image classification,” IEEE Geosci. Remote Sens. Lett. 10(4), 836–840 (2013).
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2012 (1)

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2006 (1)

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2004 (1)

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2002 (1)

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1997 (1)

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IEEE Trans. Geosci. Remote. Sens. (22)