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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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)
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)
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)
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)
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)
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)
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)
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)
Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: Proceedings of ACM International Conference on Multimedia, pp. 675–678 (2014)
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, 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)
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., Ma, C., Ni, B., Yang, X., Reid, I., Yang, M.: Deep regression tracking with shrinkage loss. In: ECCV, pp. 369–386 (2018)
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)
Ran, Q., Li, W., Du, Q.: Kernel one-class weighted sparse representation classification for change detection. Remote Sens. Lett. 9(6), 597–606 (2018)
Singh, A.: Digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989–1003 (1989)
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)
Wu, C., Zhang, L., Du, B.: Kernel slow feature analysis for scene change detection. IEEE Trans. Geosci. Remote Sens. 55(4), 2367–2384 (2017)
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)
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)
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)
Acknowledgment
This study was partly supported by the National Science and Technology Major Project (21-Y20A06-9001-17/18), the National Key Research and Development Program of China (No. 2018YFB0505000), the National Natural Science Foundation of China (No. 41571402), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 61221003).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ye, Q., Lu, X., Huo, H., Wan, L., Guo, Y., Fang, T. (2019). AggregationNet: Identifying Multiple Changes Based on Convolutional Neural Network in Bitemporal Optical Remote Sensing Images. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-16142-2_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16141-5
Online ISBN: 978-3-030-16142-2
eBook Packages: Computer ScienceComputer Science (R0)