Skip to main content

2019 | OriginalPaper | Buchkapitel

AggregationNet: Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images

verfasst von : Qiankun Ye, Xiankai Lu, Hong Huo, Lihong Wan, Yiyou Guo, Tao Fang

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

The detection of multiple changes (i.e., different change types) in bitemporal remote sensing images is a challenging task. Numerous methods focus on detecting the changing location while the detailed “from-to” change types are neglected. This paper presents a supervised framework named AggregationNet to identify the specific “from-to” change types. This AggregationNet takes two image patches as input and directly output the change types. The AggregationNet comprises a feature extraction part and a feature aggregation part. Deep “from-to” features are extracted by the feature extraction part which is a two-branch convolutional neural network. The feature aggregation part is adopted to explore the temporal correlation of the bitemporal image patches. A one-hot label map is proposed to facilitate AggregationNet. One element in the label map is set to 1 and others are set to 0. Different change types are represented by different locations of 1 in the one-hot label map. To verify the effectiveness of the proposed framework, we perform experiments on general optical remote sensing image classification datasets as well as change detection dataset. Extensive experimental results demonstrate the effectiveness of the proposed method.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R.R.: DeepSat - a learning framework for satellite imagery. CoRR abs/1509.03602 (2015) Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., Nemani, R.R.: DeepSat - a learning framework for satellite imagery. CoRR abs/1509.03602 (2015)
2.
Zurück zum Zitat Benedek, C., Sziranyi, T.: Change detection in optical aerial images by a multilayer conditional mixed markov model. IEEE Trans. Geosci. Remote Sens. 47(10), 3416–3430 (2009)CrossRef Benedek, C., Sziranyi, T.: Change detection in optical aerial images by a multilayer conditional mixed markov model. IEEE Trans. Geosci. Remote Sens. 47(10), 3416–3430 (2009)CrossRef
3.
Zurück zum Zitat Bovolo, F., Bruzzone, L.: A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Trans. Geosci. Remote Sens. 45(1), 218–236 (2007)CrossRef Bovolo, F., Bruzzone, L.: A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain. IEEE Trans. Geosci. Remote Sens. 45(1), 218–236 (2007)CrossRef
4.
Zurück zum Zitat Bovolo, F., Marchesi, S., Bruzzone, L.: A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Trans. Geosci. Remote Sens. 50(6), 2196–2212 (2012)CrossRef Bovolo, F., Marchesi, S., Bruzzone, L.: A framework for automatic and unsupervised detection of multiple changes in multitemporal images. IEEE Trans. Geosci. Remote Sens. 50(6), 2196–2212 (2012)CrossRef
5.
Zurück zum Zitat Che, M., Du, P., Gamba, P.: 2- and 3-D urban change detection with Quad-PoISAR data. IEEE Geosci. Remote Sens. Lett. 15(1), 68–72 (2018)CrossRef Che, M., Du, P., Gamba, P.: 2- and 3-D urban change detection with Quad-PoISAR data. IEEE Geosci. Remote Sens. Lett. 15(1), 68–72 (2018)CrossRef
6.
Zurück zum Zitat Gong, M., Niu, X., Zhang, P., Li, Z.: Generative adversarial networks for change detection in multispectral imagery. IEEE Geosci. Remote Sens. Lett. 14(12), 2310–2314 (2017)CrossRef Gong, M., Niu, X., Zhang, P., Li, Z.: Generative adversarial networks for change detection in multispectral imagery. IEEE Geosci. Remote Sens. Lett. 14(12), 2310–2314 (2017)CrossRef
7.
Zurück zum Zitat Gong, M., Zhao, J., Liu, J., Miao, Q., Jiao, L.: Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 27(1), 125–138 (2016)MathSciNetCrossRef Gong, M., Zhao, J., Liu, J., Miao, Q., Jiao, L.: Change detection in synthetic aperture radar images based on deep neural networks. IEEE Trans. Neural Netw. Learn. Syst. 27(1), 125–138 (2016)MathSciNetCrossRef
8.
Zurück zum Zitat Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1735–1742 (2006) Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1735–1742 (2006)
9.
Zurück zum Zitat He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
10.
Zurück zum Zitat Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of ACM International Conference on Multimedia, pp. 675–678 (2014) Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of ACM International Conference on Multimedia, pp. 675–678 (2014)
11.
Zurück zum Zitat Liu, G., Delon, J., Gousseau, Y., Tupin, F.: Unsupervised change detection between multi-sensor high resolution satellite images. In: European Signal Processing Conference, pp. 2435–2439 (2016) Liu, G., Delon, J., Gousseau, Y., Tupin, F.: Unsupervised change detection between multi-sensor high resolution satellite images. In: European Signal Processing Conference, pp. 2435–2439 (2016)
12.
Zurück zum Zitat Liu, J., Gong, M., Qin, K., Zhang, P.: A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 545–559 (2018)MathSciNetCrossRef Liu, J., Gong, M., Qin, K., Zhang, P.: A deep convolutional coupling network for change detection based on heterogeneous optical and radar images. IEEE Trans. Neural Netw. Learn. Syst. 29(3), 545–559 (2018)MathSciNetCrossRef
13.
Zurück zum Zitat Lu, X., Guo, Y., Liu, N., Wan, L., Fang, T.: Non-convex joint bilateral guided depth upsampling. Multimed. Tools Appl. 10, 1–24 (2017) Lu, X., Guo, Y., Liu, N., Wan, L., Fang, T.: Non-convex joint bilateral guided depth upsampling. Multimed. Tools Appl. 10, 1–24 (2017)
14.
Zurück zum Zitat Lu, X., Ma, C., Ni, B., Yang, X., Reid, I., Yang, M.: Deep regression tracking with shrinkage loss. In: ECCV, pp. 369–386 (2018) Lu, X., Ma, C., Ni, B., Yang, X., Reid, I., Yang, M.: Deep regression tracking with shrinkage loss. In: ECCV, pp. 369–386 (2018)
15.
Zurück zum Zitat Lv, P., Zhong, Y., Zhao, J., Zhang, L.: Unsupervised change detection model based on hybrid conditional random field for high spatial resolution remote sensing imagery. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 1863–1866 (2016) Lv, P., Zhong, Y., Zhao, J., Zhang, L.: Unsupervised change detection model based on hybrid conditional random field for high spatial resolution remote sensing imagery. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 1863–1866 (2016)
16.
Zurück zum Zitat Ran, Q., Li, W., Du, Q.: Kernel one-class weighted sparse representation classification for change detection. Remote Sens. Lett. 9(6), 597–606 (2018)CrossRef Ran, Q., Li, W., Du, Q.: Kernel one-class weighted sparse representation classification for change detection. Remote Sens. Lett. 9(6), 597–606 (2018)CrossRef
17.
Zurück zum Zitat Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)CrossRef Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)CrossRef
18.
Zurück zum Zitat Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10, 000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014) Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10, 000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014)
19.
Zurück zum Zitat Wu, C., Zhang, L., Du, B.: Kernel slow feature analysis for scene change detection. IEEE Trans. Geosci. Remote Sens. 55(4), 2367–2384 (2017)CrossRef Wu, C., Zhang, L., Du, B.: Kernel slow feature analysis for scene change detection. IEEE Trans. Geosci. Remote Sens. 55(4), 2367–2384 (2017)CrossRef
20.
Zurück zum Zitat Zhan, Y., Fu, K., Yan, M., Sun, X., Wang, H., Qiu, X.: Change detection based on deep siamese convolutional network for optical aerial images. IEEE Geosci. Remote Sens. Lett. 14(10), 1845–1849 (2017)CrossRef Zhan, Y., Fu, K., Yan, M., Sun, X., Wang, H., Qiu, X.: Change detection based on deep siamese convolutional network for optical aerial images. IEEE Geosci. Remote Sens. Lett. 14(10), 1845–1849 (2017)CrossRef
21.
Zurück zum Zitat Zhang, H., Gong, M., Zhang, P., Su, L., Shi, J.: Feature-level change detection using deep representation and feature change analysis for multispectral imagery. IEEE Geosci. Remote Sens. Lett. 13(11), 1666–1670 (2016)CrossRef Zhang, H., Gong, M., Zhang, P., Su, L., Shi, J.: Feature-level change detection using deep representation and feature change analysis for multispectral imagery. IEEE Geosci. Remote Sens. Lett. 13(11), 1666–1670 (2016)CrossRef
22.
Zurück zum Zitat Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: Amulet: aggregating multi-level convolutional features for salient object detection. In: IEEE International Conference on Computer Vision (2017) Zhang, P., Wang, D., Lu, H., Wang, H., Ruan, X.: Amulet: aggregating multi-level convolutional features for salient object detection. In: IEEE International Conference on Computer Vision (2017)
Metadaten
Titel
AggregationNet: Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images
verfasst von
Qiankun Ye
Xiankai Lu
Hong Huo
Lihong Wan
Yiyou Guo
Tao Fang
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-16142-2_29