References
- Abdikan, S., et al., 2014. A comparative data-fusion analysis of multi-sensor satellite images. International Journal of Digital Earth, 7 (8), 671–687. doi:https://doi.org/10.1080/17538947.2012.748846
- Aiazzi, B., Baronti, S., and Selva, M., et al., 2007. Improving component substitution pansharpening through multivariate regression of MS+pan data. IEEE Transactions on Geoscience and Remote Sensing, 45 (10), 3230–3239. doi:https://doi.org/10.1109/TGRS.2007.901007
- Azarang, A., and Ghassemian,H. 2018. “Application of Fractional-order Differentiation in Multispectral Image Fusion.” Remote Sensing Letters, 9 (1), 91–100. doi:https://doi.org/10.1080/2150704X.2017.1395963.
- Bhatnagar, G., Wu, Q.J., and Liu, Z., et al., 2013. Directive contrast based multimodal medical image fusion in NSCT domain. IEEE Transactions on Multimedia, 15 (5), 1014–1024. doi:https://doi.org/10.1109/TMM.2013.2244870
- Boutarfa, S., Bouchemakh, L., and Smara, Y., et al., 2019. Polarimetric synthetic aperture radar speckle filtering by multiscale edge detection. Journal of Applied Remote Sensing, 13 (2), 1–22. doi:https://doi.org/10.1117/1.JRS.13.024507
- Ghassemian, H. 2016. “A Review of Remote Sensing Image Fusion Methods.” Information Fusion, 32, 75–89. doi:https://doi.org/10.1016/j.inffus.2016.03.003.
- Gu, Y., Wang, Y., and Li, Y., 2019. A survey on deep learning-driven remote sensing image scene understanding: Scene classification, scene retrieval and scene-guided object detection. Applied Sciences, 9 (10), 2110–2134.
- Guo, Y., 2019. “Algorithm research on improving activation function of convolutional neural networks.” Chinese Control And Decision Conference (CCDC), Nanchang, China, 3582–3586.
- He, H., 2019. Learning to match multitemporal optical satellite images using multi-support-patches siamese networks. Remote Sensing Letters, 10 (6), 516–525. doi:https://doi.org/10.1080/2150704X.2019.1577572
- Hughes, L.H., 2019. “Deep learning for SAR-optical image matching.” IGARSS IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 4877–4880.
- Karathanassi, V., Kolokousis, P., and Ioannidou, S., et al., 2007. A comparison study on fusion methods using evaluation indicators. International Journal of Remote Sensing, 28 (10), 2309–2341. doi:https://doi.org/10.1080/01431160600606890
- Keshtkar, H., Voigt, W., and Alizadeh, E., et al., 2017. Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery. Arabian Journal of Geosciences, 10 (6), 1–15. doi:https://doi.org/10.1007/s12517-017-2899-y
- Kulkarni, S.C. and Rege, P.P., 2020. Pixel level fusion techniques for SAR and optical images: a review. Information Fusion, 59, 13–29. doi:https://doi.org/10.1016/j.inffus.2020.01.003
- Laben, C.A. and Brower, B.V., 2000. Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. United States, 1–9.
- Lal, A.M. and Anouncia, S.M., 2016. Enhanced dictionary based sparse representation fusion for multi-temporal remote sensing images. European Journal of Remote Sensing, 49 (1), 317–336. doi:https://doi.org/10.5721/EuJRS20164918
- LeCun, Y., 1998. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE, 86, 2278–2324. 11.
- Lee, J., 2020. Different spectral domain transformation for land cover classification using convolutional neural networks with multi-temporal satellite imagery. Remote Sensing, 12 (7), 1097–1125. doi:https://doi.org/10.3390/rs12071097
- Li, Y., Zhang, H., and Shen, Q., et al., 2017. Spectral–spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sensing, 9 (1), 67–88. doi:https://doi.org/10.3390/rs9010067
- Li, Y., Zhang, H., Xue, X., Jiang, Y., and Shen, Q., 2018. Deep learning for remote sensing image classification: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8 (6), 1264–1281.
- Liu, J.G., 2000. Smoothing filter-based intensity modulation: a spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21 (18), 3461–3472. doi:https://doi.org/10.1080/014311600750037499
- Liu, Y., 2017a. “A medical image fusion method based on convolutional neural networks.” International Conference on Information Fusion (Fusion), China, 1–7.
- Liu, Y., 2017b. Multi-focus image fusion with a deep convolutional neural network. Information Fusion, 36, 191–207. doi:https://doi.org/10.1016/j.inffus.2016.12.001
- Liu, Y., 2018. Infrared and visible image fusion with convolutional neural networks. International Journal of Wavelets, Multiresolution and Information Processing, 16 (3), 1850018–1850037. doi:https://doi.org/10.1142/S0219691318500182
- Liu, Y., X. Chen, J. Cheng, H. Peng, and Z. Wang. 2017. “Multi-focus Image Fusion with a Deep Convolutional Neural Network.” Information Fusion, 36, 191–207. doi:https://doi.org/10.1016/j.inffus.2016.12.001.
- Ma, J., Ma, Y., and Li, C., 2019. Infrared and visible image fusion methods and applications: A survey. Information Fusion, 45, 153–178.
- Masi, G., 2016. Pansharpening by convolutional neural networks. Remote Sensing, 8 (7), 594–616. doi:https://doi.org/10.3390/rs8070594
- Meng, X., 2019. Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: practical discussion and challenges. Information Fusion, 46, 102–113. doi:https://doi.org/10.1016/j.inffus.2018.05.006
- Mountrakis, G., Im, J., and Ogole, C., et al., 2011. Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing, 66 (3), 247–259. doi:https://doi.org/10.1016/j.isprsjprs.2010.11.001
- Parihar, N., Rathore, V.S., and Mohan, S., et al., 2017. Combining ALOS PALSAR and AVNIR-2 data for effective land use/land cover classification in Jharia Coalfields Region. International Journal of Image and Data Fusion, 8 (2), 130–147. doi:https://doi.org/10.1080/19479832.2016.1273258
- Ren, J., 2021. An SVM-based nested sliding window approach for spectral–spatial classification of hyperspectral images. Remote Sensing, 13 (1), 114–240. doi:https://doi.org/10.3390/rs13010114
- Rußwurm, M. and Körner, M., 2018. Multi-temporal land cover classification with sequential recurrent encoders. ISPRS International Journal of Geo-Information, 7 (4), 129–147. doi:https://doi.org/10.3390/ijgi7040129
- Sameen, M.I., Pradhan, B., and Lee, S., et al., 2019. Self-learning random forests model for mapping groundwater yield in data-scarce areas. Natural Resources Research, 28 (3), 757–775. doi:https://doi.org/10.1007/s11053-018-9416-1
- Sameen, M.I., Pradhan, B., and Lee, S., et al., 2020. Application of convolutional neural networks featuring bayesian optimization for landslide susceptibility assessment. Catena, 186, 104249. doi:https://doi.org/10.1016/j.catena.2019.104249
- Saralioglu, E. and Gungor, O., 2020. Semantic segmentation of land cover from high resolution multispectral satellite images by spectral-spatial convolutional neural network. Geocarto International, 1–21. https://doi.org/https://doi.org/10.1080/10106049.2020.1734871
- Scarpa, G., 2018. A CNN- based fusion method for feature extraction from sentinel data. Remote Sensing, 10 (2), 236–256. doi:https://doi.org/10.3390/rs10020236
- Schmitt, M. and Zhu, X.X., 2016. Data fusion and remote sensing: an ever-growing relationship. IEEE Geoscience and Remote Sensing Magazine 4 (4), 6–23.
- Shakya, A., Biswas, M., and Pal, M., et al., 2020. CNN- based fusion and classification of SAR and optical data. International Journal of Remote Sensing, 41 (22), 8839–8861. doi:https://doi.org/10.1080/01431161.2020.1783713
- Shakya, A., Biswas, M., and Pal, M., et al., 2021. Parametric study of convolutional neural network based remote sensing image classification. International Journal of Remote Sensing, 42 (7), 2663–2685. doi:https://doi.org/10.1080/01431161.2020.1857877
- Sheoran, A. and Haack, B., 2014. Optical and radar data comparison and integration: Kenya example. Geocarto International, 29 (4), 370–382. doi:https://doi.org/10.1080/10106049.2013.769027
- Singh, R. and Khare, A., 2014. Fusion of multimodal medical images using Daubechies complex wavelet transform- A multiresolution approach. Information Fusion, 19, 49–60. doi:https://doi.org/10.1016/j.inffus.2012.09.005
- Song, J., Gao, S., Zhu, Y., and Ma, C., 2019. A survey of remote sensing image classification based on CNNs. Big earth data, 3 (3), 232–254.
- Tetteh, G.O., Gocht, A., and Conrad, C., et al., 2020. Optimal parameters for delineating agricultural parcels from satellite images based on supervised bayesian optimization. Computers and Electronics in Agriculture, 178, 105696–105711. doi:https://doi.org/10.1016/j.compag.2020.105696
- Tripathi, G., 2020. Flood inundation mapping and impact assessment using multi-temporal optical and SAR satellite data: a case study of 2017 flood in Darbhanga District, Bihar, India. Water Resources Management, 34 (6), 1871–1892. doi:https://doi.org/10.1007/s11269-020-02534-3
- Vapnik, V.N., 2000. The Vicinal Risk Minimization Principle and the SVMs. In: V.N. Vapnik, ed. The Nature of Statistical Learning Theory. New York: Springer New York, 267–290.
- Vivone, G., 2020. Pansharpening. In: Data Handling in Science and Technology. Vol. 32, Elsevier, 69–91. https://doi.org/https://doi.org/10.1016/B978-0-444-63977-6.00005-5
- Vivone, G., Alparone, L., Chanussot, J., Dalla Mura, M., Garzelli, A., Licciardi, G.A., Restaino, R., and Wald, L., 2014. A critical comparison among pansharpening algorithms. IEEE Transactions on Geoscience and Remote Sensing, 53 (5), 2565–2586.
- Weng, Q. and Quattrochi, D.A., 2018. Urban Remote Sensing, Second Edition. New York: CRC Press/Taylor and Francis. 2018.
- Wu, Q., 2017. A comparison of pixel-based decision tree and object-based support vector machine methods for land-cover classification based on aerial images and airborne lidar data. International Journal of Remote Sensing, 38 (23), 7176–7195. doi:https://doi.org/10.1080/01431161.2017.1371864
- Yang, L. and Shami, A., 2020. On hyperparameter optimization of machine learning algorithms: theory and practice. Neurocomputing, 415, 295–316. doi:https://doi.org/10.1016/j.neucom.2020.07.061
- Zhang, T., 2021. Sentinel-2 satellite imagery for urban land cover classification by optimized random forest classifier. Applied Sciences, 11 (2), 543–560. doi:https://doi.org/10.3390/app11020543
- Zhong, L., Hu, L., and Zhou, H., et al., 2019. Deep learning based multi-temporal crop classification. Remote Sensing of Environment, 221, 430–443. doi:https://doi.org/10.1016/j.rse.2018.11.032
- Zhu, X.X., Grohnfeldt, C., and Bamler, R., et al., 2015. Exploiting joint sparsity for pansharpening: the J-SparseFI algorithm. IEEE Transactions on Geoscience and Remote Sensing, 54 (5), 2664–2681. doi:https://doi.org/10.1109/TGRS.2015.2504261
- Zhu, X.X., Tuia, D., Mou, L., Xia, G.S., Zhang, L., Xu, F., and Fraundorfer, F., 2017. Deep learning in remote sensing: A comprehensive review and list of resources. IEEE Geoscience and Remote Sensing Magazine, 5 (4), 8–36.