The paper considers the problem of classification of content of multitemporal, multimodal and multispectral remote sensing images, with the aim of settlement detection. The accent is on the integration of data from multiple different sources.
References
[1] C. Kyba et al., "High-Resolution Imagery of Earth at Night: New Sources, Opportunities and Challenges", Remote Sensing, vol. 7, no. 1, pp. 1-23, 2014. Dostupno: 10.3390/rs70100001
[2] P. Benedetti, D. Ienco, R. Gaetano, K. Ose, R. Pensa and S. Dupuy, "M3Fusion: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 12, pp. 4939-4949, 2018. Dostupno: 10.1109/jstars.2018.2876357
[3] L. El Mendili, A. Puissant, M. Chougrad and I. Sebari, "Towards a Multi-Temporal Deep Learning Approach for Mapping Urban Fabric Using Sentinel 2 Images", Remote Sensing, vol. 12, no. 3, p. 423, 2020. Dostupno: 10.3390/rs12030423
[4] N. Mboga, C. Persello, J. Bergado and A. Stein, "Detection of informal settlements from VHR satellite images using convolutional neural networks", 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2017. Dostupno: 10.1109/igarss.2017.8128166
[5] 2021 IEEE GRSS Data Fusion Contest: www.grss-ieee.org/community/technical-committees/data-fusion”.