Deep Learning-based Detection of Feature Changes in Arable Land
DOI:
https://doi.org/10.36777/jag2023.2.1.4Abstract
In economically developed regions, the occupation of arable land has long been a significant concern, and identifying cultivated land accurately is crucial for mitigating this issue. To address the problem of "pseudo-change" caused by complex backgrounds, uneven brightness, and natural growth, we propose a change detection algorithm based on twin neural networks. Our algorithm comprises two sub-networks and a decision layer that use a multi-scale feature model and an improved spatio-temporal attention module to extract and process features from two temporal images. We validate our algorithm using high-resolution UAV aerial photography data with a resolution of 0.3m and 0.05m. Our proposed method achieves an F1 coefficient of over 87% in the cultivated land feature change detection dataset, which is more than 45% higher than the traditional STANet. The contribution of our research lies in developing a more effective method for identifying cultivated land in economically developed regions, which can help mitigate the serious "man-made disaster" of arable land occupation. Our proposed method has the potential to contribute significantly to the field of precision agriculture and land resource management.
References
Beke, Á. K., Gyürkés, M., Nagy, Z. K., Marosi, G., & Farkas, A. (2021). Digital twin of low dosage continuous powder blending: Artificial neural networks and residence time distribution models. European Journal of Pharmaceutics and Biopharmaceutics, 169, 64–77. https://doi.org/10.1016/j.ejpb.2021.09.006
Chen, G., Zhang, X., Wang, Q., Dai, F., Gong, Y., & Zhu, K. (2018). Symmetrical dense-shortcut deep fully convolutional networks for semantic segmentation of very-high-resolution remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1633–1644. https://doi.org/10.1109/JSTARS.2018.2810320
Chen, H., & Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sensing, 12(10), 1662. https://doi.org/10.3390/rs12101662
Das, S., Chakraborty, S., Routray, A., & Deb, A. K. (2019). Fast linear unmixing of hyperspectral image by slow feature analysis and simplex volume ratio approach. In IGARSS 2019: 2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 560–563). IEEE. https://doi.org/10.1109/IGARSS.2019.8898127
Du, B., Wang, Y., Wu, C., & Zhang, L. (2018). Unsupervised scene change detection via latent Dirichlet allocation and multivariate alteration detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(12), 4676–4689. https://doi.org/10.1109/JSTARS.2018.2869549
Du, W., Wang, Y., & Qiao, Y. (2018). Recurrent spatial-temporal attention network for action recognition in videos. IEEE Transactions on Image Processing, 27(3), 1347–1360. https://doi.org/10.1109/TIP.2017.2778563
Gao, M., Zhou, X., Liu, Q., Yang, G., & Wu, C. (2022). Automatic monitoring of farmland occupation by farmhouse based on deep learning network. Bulletin of Surveying and Mapping, 3, 47–53. https://doi.org/10.13474/j.cnki.11-2246.2022.0076
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778). IEEE. https://doi.org/10.1109/CVPR.2016.90
Hu, B., Zhang, W., Ma, T., & Zhao, Z. (2021). Optimization and simulation of farmland protection dynamic monitoring system based on Internet of Things technology. Wireless Communications and Mobile Computing, 2021, 1–11. https://doi.org/10.1155/2021/6902998
Jabari, S., Rezaee, M., Fathollahi, F., & Zhang, Y. (2019). Multispectral change detection using multivariate Kullback–Leibler distance. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 163–177. https://doi.org/10.1016/j.isprsjprs.2018.11.014
Jin, S., Yang, L., Danielson, P., Homer, C., Fry, J., & Xian, G. (2013). A comprehensive change detection method for updating the National Land Cover Database to circa 2011. Remote Sensing of Environment, 132, 159–175. https://doi.org/10.1016/j.rse.2013.01.012
Leichtle, T., Geiß, C., Wurm, M., Lakes, T., & Taubenböck, H. (2017). Unsupervised change detection in VHR remote sensing imagery: An object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation, 54, 15–27. https://doi.org/10.1016/j.jag.2016.08.010
Liu, S., Bruzzone, L., Bovolo, F., & Du, P. (2015). Hierarchical unsupervised change detection in multitemporal hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 53(1), 244–260. https://doi.org/10.1109/TGRS.2014.2321277
Shang, C., Yang, F., Huang, B., & Huang, D. (2018). Recursive slow feature analysis for adaptive monitoring of industrial processes. IEEE Transactions on Industrial Electronics, 65(11), 8895–8905. https://doi.org/10.1109/TIE.2018.2811358
Wu, Z., Shen, C., & van den Hengel, A. (2019). Wider or deeper: Revisiting the ResNet model for visual recognition. Pattern Recognition, 90, 119–133. https://doi.org/10.1016/j.patcog.2019.01.006
Zhang, C., Yue, P., Tapete, D., Jiang, L., Shangguan, B., Huang, L., & Liu, G. (2020). A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images. ISPRS Journal of Photogrammetry and Remote Sensing, 166, 183–200. https://doi.org/10.1016/j.isprsjprs.2020.06.003
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Journal of Asian Geography
This work is licensed under a Creative Commons Attribution 4.0 International License.
